The Scientific Temper

Measuring the relationship of land use land cover, normalized difference vegetation index and land surface temperature in influencing the urban microclimate in northeast Delhi, India

2024-06-11T21:50:38+00:00

Urbanisation is a process for conversion of spaces into build up areas, long-term conversions led to shrinkage of open lands to unplanned growth for residential areas or commercial purposes to accommodate the increasing population. Open green spaces and parks have strategic role in regulate the urban environment and mitigate the effect of urban heat island (UHI). Also, its spatial arrangement in an area impacts the surface heating so their spatial distribution within the residential sectors, has become an integral part of sustainable development for the city. They have direct socio-cultural and health benefits by providing spaces for physical activity, social interaction, and fresh air, which depends upon the accessibility, quality, attractiveness, and regularity of people using it. In North East Delhi district, distribution of parks shows huge disparity between planned and unplanned residential areas which is impacting the surface temperature and micro climate. A quantitative analysis is used with Geo-informatics-based indexes of biophysical parameters for parks, vegetation quality (NDVI) and land surface temperature distribution (LST) with the land use pattern for accessing the impact of parks on mitigating the heat island effect. The result obtained from analysis shows a negative relation between the LST and NDVI. The unplanned regions have big parks with negative NDVI values (-0.04) lacking vegetation cover and are poorly maintained and accessed by many people, on the other hand the parks with NDVI values (0.12) are well distributed in planned colonies and maintained. Maximum of the existing parks is below 10km²area and has poor quality. This study shows healthy vegetation in parks is important for wellbeing of the city rather than merely open spaces.

Examining the relationship between motivation and incentives in the context of maternal health awareness: A study of Asha workers in Uttarakhand

2024-08-09T14:42:27+00:00

ASHA workers have been facing deprivation in the context of remuneration. The incentive paid to the ASHA workers is not sufficient for the amount of work ASHA workers are performing in the fields tirelessly. Several newspapers like The Times of India, Deccan Herald, and The Hindu etc., have covered the issues that ASHA workers have raised despite their dedication towards their services. ASHA workers have also gone for strikes citing their poor conditions. Jain et al. (2022) have constructed an earnings projection model and incentive structure that affect the motivation of community health workers. The ground-level issues pertain to challenges such as lack of motivation due to less incentive for the tasks performed. During the COVID-19 pandemic, Community health workers continued to provide services ranging from vaccinations to folic tablets to pregnant women. This paper discusses the plight of the community health workers (ASHA) in the select villages of Bageshwar district in Uttarakhand. The cross-sectional study was conducted from Feb 2021 to Aug 2023 in Bilonasera, Kathayatbara, and Manyura Mafi of Bageshwar districts. The field study discusses the narratives of the challenges and issues faced by grassroots-level workers. The study highlights the case –studies of the issues faced by the community health workers. Further, content analysis of the newspapers was done to explore the issue and reach the investigation. The study also presents recommendations on reforms in the wage structure and benefits to the accredited social health activists (ASHA).

Exploring the mediating role of gastronomic experience in tourist satisfaction: A multigroup analysis

2024-06-27T08:27:57+00:00

Gastronomic experience encountered by tourists in a famous local food outlet plays a crucial role in delivering overall satisfaction with the destination. Previous studies widely acknowledge the importance of region’s cuisine in enhancing gastronomic tourism, and have identified various types of tourists depending upon their interest in local food preparations. This empirical analysis presents a novel approach in investigating the mediating role of gastronomic experience in the relation between gastronomic motivation, and overall satisfaction of the tourists who are segmented according to the relevance of local gastronomy in their destination selection, in context of famous local food outlets which offer delectable traditional Punjabi cuisine in the holy city of Amritsar, proclaimed as the food capital of Punjab. Data was gathered through a well-structured, and self-administered survey questionnaire circulated amongst the tourists after their gastronomic encounter. The constructs of the study were specified as reflective or formative as per the nature of their measurement indicators. Hierarchical and K-means cluster analysis was used for segmentation, and PLS-SEM was further utilized to conduct Multigroup analysis, after ascertaining the common method bias, and measurement invariance using the MICOM process. The results reveal full mediation exhibited by gastronomic experience, and an insignificant difference between the tourist segments on the strength of proposed relationships amongst the study’s constructs. Implications and suggestions are provided for the owners, and managers of local food outlets, the government and all other stakeholders linked to the enhancement of tourist experience at the destination. Future studies may replicate the current model in other tourist destinations, to further validate the findings.

Combination of financial literacy, strategic marketing and effective human resource for sustainable household wealth development

2024-07-27T07:44:11+00:00

Financial literacy encompasses not only knowledge of financial terminology and basic concepts but also the ability to comprehend, analyse, and make informed decisions about financial options. As individuals navigate a complex financial environment characterized by new products, market complexities, and global influences, the need for financial literacy becomes paramount. In the contemporary financial landscape, the concept of financial literacy has garnered increasing attention due to its profound implications for individual and societal prosperity. This study delves into how financial literacy, combined with strategic marketing and effective human resource management, contributes to sustainable household wealth development, examining its impact on prudent decision-making, effective financial planning, and the ability to respond to economic dynamics.

The present study encompasses a mixed-methods approach, combining quantitative and qualitative research techniques. A structured questionnaire including Likert scale questions, is administered to a diverse sample of participants, ensuring representation across various demographics. The survey aims to gauge the current level of financial literacy, understand financial behaviours, and assess the impact of financial literacy on decision-making related to wealth management. Data analysis involved both quantitative and qualitative methods, such as percentage analysis, to identify level and relations, and qualitative content analysis to derive themes and narratives from responses. Drawing on research findings, the study explored the linkage between financial literacy and sustainable wealth development, shedding light on its multifaceted influence on financial behaviours, investment choices, and overall economic well-being

Inclusive education for children with learning difficulties in Mauritius: An analytical study among select stakeholders

2024-08-29T11:49:34+00:00

The present research explores the intricate terrain of inclusive education in Mauritius, providing insightful viewpoints on the implemented practices, policies, and support networks. Through the integration of quantitative and qualitative research approaches, this study offers a thorough examination of the situation of inclusive education, allowing for the identification of learning-disabled children in Mauritius. The results offer useful information that help improve policies and regulations to improve these kids' educational outcomes and experiences. Using both quantitative and qualitative methods, this study examines inclusive education for kids with learning disabilities in Mauritius in great detail. Parents, educators, and representatives from pertinent organizations participate in the research. The research delves deeply into their viewpoints, issues, and experiences. Twenty-five parents of children with learning disabilities participated in a survey that was part of the study's quantitative component. The purpose of the study was to collect quantitative information about parents' experiences and viewpoints on inclusive education in Mauritius. The qualitative phase involved conducting in-depth interviews with a wide range of individuals. ECCEA, SENA, speech and language pathologists, one integrated school, five parents, ten non-profit special education teachers, and a representative from academics were among them. These interviews were carried out in order to acquire more data on the various facets of inclusive education, including the difficulties encountered, workable solutions, and the duties of various stakeholders.

Exploring the expansion trajectory of the Indian automobile sector

2024-06-25T13:40:22+00:00

The Indian Automobile sector stands as a key element of the nation's economy, having accomplished remarkable growth and variation over time. This sector has display incredible disparity as it includes two- and three-wheelers, commercial vehicles, and passenger vehicles. Furthermore, India categorizes as massive manufacturer of passenger cars, commercial vehicles, three- wheelers and two- wheelers. The Indian economy was confined before liberalization period, expressed by high import tariffs meant to shield the country's auto sector. The sector's enlargement was hampered by this constraint, moreover the domination of a few firms. But along the 1991 liberalisation policy, the industry reflects a sharp boost in growth, which unfold new way and quickened its development. The prime data source for this research was the Annual Survey of Industries. The growth rates of various automotive sector variables were analysed in this article.

Agent’s roles and perspectives of life insurance market in North-East India

2024-08-02T05:38:19+00:00

The study explores the roles and perspectives of life insurance agents as they are the dominant channel for distribution of policies. It uses qualitative primary data collected from 13 veteran agents and 4 managers of LIC in North-Eastern states using a semi-structured interview schedule. The main focus of the interview was to obtain extensive information regarding their experience as life insurance agents, with clients and with the company LIC itself. Assam has the most vibrant life insurance market, with better awareness and penetration across varying occupations and geographical areas. The agents prioritized building a good relationship with their customers, establishing a sense of trust with the company, and providing good after-sales services. Digitization has played a big role in catering to these responsibilities and has changed the work culture of insurance agents. There is stiff competition with the private sector, and the culture in each state affects spending and saving habits. Irregular income and mis-selling are the main causes of lapsation of policies.

Navigating fake reviews in online marketing: Innovative strategies for authenticity and trust in the digital age

2024-08-16T16:26:41+00:00

The prevalence of fake reviews and ratings in online marketing has become a significant issue, undermining consumer trust and damaging business reputations. This study aims to identify the extent and impact of fake reviews and explore innovative strategies for traders to combat this problem and maintain authenticity. A mixed-method approach was adopted, including a survey of 100 respondents to gauge public awareness and perception of fake reviews. The findings highlight the critical need for advanced detection methods, increased transparency, and consumer education to foster trust in online platforms. Statistical analyses, including ANOVA and Chi-Square tests, were used to analyze the data. The survey revealed that 75% of respondents encounter fake reviews frequently, with 40% being highly aware of the issue. The impact of fake reviews on purchasing decisions is significant, affecting 80% of the respondents. To address this issue, traders can implement advanced AI algorithms, encourage genuine reviews through incentives, increase transparency through verification processes, and collaborate with review platforms to establish stricter monitoring systems. Additionally, educating consumers about identifying fake reviews and promoting ethical online behavior are crucial steps towards mitigating this issue. This study concludes that by adopting these innovative strategies, traders can protect their reputations, foster consumer trust, and ensure the authenticity of online reviews and ratings. Future research should focus on developing more sophisticated detection technologies and exploring the long-term effects of fake reviews on consumer behavior and market dynamics.

Empowering Indian consumers to embrace electric vehicles through the unified theory of acceptance and use of technology

2024-07-16T08:40:42+00:00

The Indian transport sector accounts for the highest share of greenhouse gas emissions. Traditional vehicles replacing with electric ones are India's only viable solution to reduce greenhouse gases. “Electric Vehicles (EVs)” might significantly lessen the negative effects of the transportation sector on the environment. In this research, we use a UTAUT model to assess consumer intent to embrace EVs as a means of transportation. “Data from 200 Indian respondents were collected using a purposive sampling strategy, and the results were analyzed using the Amos structural equation modelling technique”. According to the findings, there is a considerable impact of “Performance Expectancy,” “Effort Expectancy”, “Social Influence”, “Facilitating Conditions”, and “Price Value” on consumer adoption intentions for “electric vehicles”. The findings of this study will provide valuable insights for policymakers and manufacturers in developing effective marketing tactics that enhance “Customer Motivation, Awareness, and Value Generation” for “electric vehicles for sustainable development.

Study on the right to education with special references to public private partnerships

2024-07-01T13:13:42+00:00

The Indian education system faces significant challenges in providing quality education to all its citizens, particularly in the context of limited government resources. Public-private partnerships (PPPs) have emerged as a critical strategy to bridge this gap, leveraging private sector participation in financing and managing educational institutions. This paper examines the evolving landscape of education under PPP models in India, focusing on the implications for the right to education. Through a comprehensive review of literature and analysis of case studies, the study explores how PPPs influence access, equity, and quality in education, while also addressing concerns related to privatization and accountability. By evaluating the successes, challenges, and policy implications of PPPs in education, this research contributes to the broader discourse on the role of private sector involvement in fulfilling the right to education in developing countries.

Barriers to last mile connectivity: The role of crime in metro station accessibility

2024-08-15T07:52:23+00:00

The accessibility of metro stations is a crucial aspect of urban mobility, yet last-mile connectivity often faces significant barriers, particularly due to crime and safety concerns. This study examines the role of crime as a determinant of last-mile connectivity to metro stations, focusing on how criminal activities and the fear of crime influence commuter behavior and accessibility. Through a mixed-methods approach, the research integrates quantitative analysis of crime data around metro stations with qualitative insights from commuter surveys and interviews with urban planners and law enforcement officials. The findings reveal that higher crime rates and perceived safety risks significantly reduce commuters' willingness to use metro systems, particularly during early morning and late evening hours. This reduced accessibility not only hampers the efficiency of metro systems but also exacerbates issues related to traffic congestion, environmental pollution, and social inequality. The study further explores the economic impact of crime on areas surrounding metro stations, highlighting how declining property values and business activity contribute to a cycle of disinvestment and increased crime. To address these challenges, the study suggests a comprehensive approach combining enhanced surveillance, improved lighting, community engagement, and urban design interventions. These strategies are critical for creating safer, more accessible environments that encourage the use of public transportation and promote sustainable urban development. The research provides valuable insights for urban planners, policymakers, and scholars interested in improving last-mile connectivity and enhancing the overall quality of urban life.

Exploring the landscape of brand extensions: A bibliometric analysis of scholarly trends and insights

2024-07-12T15:35:14+00:00

This bibliometric analysis offers scholarly insights into brand extension research by examining academic output, influential sources, leading authors, and keyword co-occurrences in the field. Analyzing a dataset spanning from 2014 to 2023, the study reveals significant trends, patterns, and themes. The annual distribution of research output shows fluctuations, indicating evolving research priorities. In terms of institutional productivity, the School of Management at Zhejiang University is a leading contributor, followed closely by the Business School at Hanyang University and Dankook University, underlining their significant scholarly influence. Key sources such as the "Journal of Business Research," "Journal of Brand Management," and "Journal of Product and Brand Management" shape the discourse, emphasizing their pivotal role in brand extension exploration. Co-citation analysis identifies Keller K.L., Aaker D.A., Park C.W., and Loken B. as the most influential authors, forming the intellectual backbone of the field. Keyword co-occurrence analysis uncovers vital concepts like "brand equity," "brand management," and "perceived fit," shedding light on critical themes in brand extension research. This study enhances our understanding of brand extension literature, serving as a valuable resource for researchers and practitioners seeking to explore the evolving landscape of this dynamic field.

The craft of portfolio construction in estate planning: A comprehensive review on equity and mutual fund strategies, and its risks

2024-08-30T08:02:41+00:00

Investing in equity and mutual funds can play a significant role in estate planning, helping individuals grow their wealth while ensuring that their financial legacy is effectively managed and passed on to heirs. Understanding how these investment vehicles function within an estate plan can lead to more strategic decision-making and better outcomes for beneficiaries. As popular means of investing, Equity and Mutual funds have drawn a variety of investors looking for portfolio diversity and expert management. This summary gives a general review of Equity & mutual funds, looking at their features, advantages, and drawbacks. It examines the major investment options, including stock, and balanced funds, as well as the primary variables affecting their performance. The hazards linked to equity & mutual funds, such as market risk, liquidity risk, and regulatory risk, are also highlighted in this abstract. This abstract seeks to provide readers with an in-depth understanding of Equity & Mutual funds so they may make informed financial decisions.

A critical review of social media advertising literature: Visualization and bibliometric approach

2024-07-08T07:27:58+00:00

This bibliometric analysis delves into the landscape of research on "social media advertising" spanning from 2012 to 2023, presenting significant findings that shed light on the field's evolution and scholarly contributions. The study observes a consistent annual growth rate of 6.5%, indicating a sustained interest in exploring the ever-changing realm of social media advertising. Notably, the relatively young average document age of 4.28 years reflects the proactive nature of researchers in keeping pace with contemporary developments. The analysis highlights the substantial impact of research efforts in this domain, with an average citation count of 58.01 per document and an extensive total number of references amounting to 7,073. The significant international co-authorship percentage of 34% emphasizes the global outlook of the discipline and the collaborative nature of knowledge creation across borders. Among academic sources, the "Journal of Research in Interactive Marketing" emerges as a prominent contributor, with notable influence demonstrated by its 12 documents and 698 citations. Other influential journals such as "Computers in Human Behavior" and "Internet Research" follow closely behind. Additionally, the study identifies leading authors and organizations in the field, particularly highlighting the dominant role of the United States in research productivity, international collaboration, and overall research impact. In summary, this bibliometric analysis offers a comprehensive overview of social media advertising, showcasing its growth, international collaboration, focus on contemporary research, and substantial influence. These insights hold significance for researchers, institutions, and policymakers, shaping the future trajectory of this dynamic field and ensuring its continued relevance and global impact.

Direct selling laws and regulations in India: A comprehensive study

2024-06-26T12:25:22+00:00

Direct selling is a long-standing and widely utilized business concept in which salespeople engage directly with customers to generate sales through personalized interaction. Even though direct selling has been a thriving sector in India for a long time, official regulatory attempts have only lately gained pace in the last few decades. By examining the regulatory frameworks already in place, this research aims to analyze the regulatory environment of the direct selling industry. We look at the challenges caused by conflicting regulations resulting from many legislative attempts and the impact these limitations have on the industry. We also look into the limitations of the current regulatory framework, which depends on collaboration between the Indian central government (which functions similarly to the U.S. federal government) and the state governments it oversees. Finally, we will provide some solutions that might address these issues and raise the effectiveness of the industry's regulatory system.

Unveiling scholarly insights: A bibliometric analysis of literature on gender bias at the workplace

2024-06-14T06:14:28+00:00

Gender bias and discrimination in the workplace remain significant global challenges, impacting individuals and organizations. Despite heightened awareness and scholarly focus, a comprehensive, up-to-date evaluation of the literature’s scientific impact and citation trends is missing. This research article addresses this gap through a bibliometric analysis from 2000 to 2023, assessing gender bias’s scientific significance, citations, and pre-publication information. Utilizing tools like RStudio, VOS viewer, Dimensions analytics, and MS Excel, the study analyzes manuscripts from the Dimensions database. The analysis reveals notable trends, showing a steady rise in publications from 2003, with fluctuations in 2002 and 2008-2011, stability from 2012-2015, and a significant surge from 2016-2023, peaking in 2019-2022. The United States leads in publication quantity and collaboration. Key topics such as "Economics and Identity," the "glass cliff phenomenon," and the "climate for women in academic science" dominate citations. Prominent journals like "Building A New Leadership Ladder" and "Plos One" highlight the interdisciplinary nature of gender bias research. Influential contributors like Geffner CJ, Kim S, and Ryan MK are acknowledged for their dedication. This study underscores the interdisciplinary reach of gender bias research across Human Society, Commerce, Law, Biomedical Sciences, and Psychology, offering valuable insights into publication trends, collaborative networks, and thematic developments. The findings emphasize the need for continued exploration and collaboration to address gender-related challenges in professional settings.

Dark web exploitation of women and children: Understanding the phenomenon and combating its impact

2024-08-26T13:13:13+00:00

The hidden nature of the dark web makes it easier to exploit vulnerable groups, particularly women and children. This research explores how they are exploited on the dark web, including what causes it, common ways it happens, the difficulties law enforcement encounters, and steps to prevent and address it. By combining existing research, case studies, and expert opinions, this work aims to provide a comprehensive understanding of the issue and suggest practical solutions. While the internet has brought positive changes, its hidden parts like the Dark Web pose significant risks to vulnerable people. This study sheds light on the dangers women and children face online, such as human trafficking, child exploitation, and cyber bullying. Its goal is to safeguard their online safety and well-being by promoting prevention and policy adoption.

An optimized approach for detection and mitigation of DDoS attack cloud using an ensembled deep learning approach

2024-07-12T19:37:10+00:00

As cloud computing gains in popularity, safety becomes an increasingly important consideration. One of the most challenging issues in cloud computing is the detection of Distributed Denial-of-Service (DDoS) attacks (Gupta, B. B., et al., 2009). One of the most crucial aspects of cloud architecture is the ability to provide self-service whenever it is needed. Applications built on the cloud computing model are available on demand and at low cost. As cloud computing grows in popularity, so too is the amount of cyberattacks aimed against it. One such attack is a Distributed Denial of Service attack, which is designed to overload the cloud's hardware/software, resources, and services, making them difficult to use for everyone. The difficulty of this assault stems from the fact that it can overwhelm the victim's ability to communicate or compute in a short amount of time with little to no notice. It's getting harder to spot and stop these assaults as they get more sophisticated and more numerous. Several Machine Learning methods, including Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Naive Bayes, Multi-layer Perceptron, XGBoost, and SGD have been implemented for accurate DDoS flooding attack detection. When compared to current methods, the suggested strategy of utilizing deep learning with Quadratic discriminant appears to result in higher accuracy. There is also a thorough comparison and evaluation of the abovementioned algorithms with respect to the accuracy measures used.

Multi-objective nature inspired hybrid optimization algorithm to improve prediction accuracy on imbalance medical datasets

2024-08-12T14:49:27+00:00

Imbalanced medical datasets pose a significant challenge for predictive modelling. The current study presents a new method of performing feature selection specifically for the imbalanced medical datasets to improve accuracy of the predictions. The proposed Multi-Objective Feature Selection with Cost-Sensitive (MOFSCS)algorithm leverages the large-scale exploration capability of the Squirrel Search to generate diverse candidate feature subsets and employs Tabu Search for local optima refinement. One of the key developments is learning with consideration of costs, which is closer to the identification of the minority class. The effectiveness of the proposed approach is ensured by the experiments on different imbalanced medical datasets, namely, heart disease and stroke prediction datasets. The results reveal that the proposed method, when integrated with the XGBoost classifier, achieves a precision of 98.5%, recall of 98.7%, F1-score of 98.6%, accuracy of 98.7%, and an AUC-ROC of 98.7% on the heart disease dataset. Similarly, for brain stroke prediction, the model attains a precision of 98.9%, recall of 99.0%, F1-score of 98.9%, accuracy of 99.0%, and an AUC-ROC of 99.0%.

A novel approach for metrics-based software defect prediction using genetic algorithm

2024-08-22T05:40:21+00:00

Software defect prediction is an important issue in the process of software development and maintenance, which is related to the overall success or failure of software. This is because early software failure prediction can improve software quality, reliability and efficiency, and reduce software cost. However, developing robust defect prediction models is a challenging task and many techniques have been proposed in the literature. In this paper, a software defect prediction model based on Novel Hybrid Genetics Software Defect Prediction (NHGSDP) is proposed. The supervised NHGSDP algorithm has been used to predict future software failures based on historical data. The evaluation process shows that the NHGSDP algorithm can be used effectively with high accuracy. The collected results show that the NHGSDP method has better performance.

A self-regulating optimization algorithm for locating and sizing a local power generation source for a radial structured distribution system in deregulated environment

2024-07-01T09:05:07+00:00

The Indian power sector is a large and complex network. Maintaining that complex network with the present regulatory format is very difficult for the government as well as transco and discom companies in terms of cost, efficiency, and reliability. That is why the government encourages deregulation in the power sector. One of the deregulation concepts is the integration of local sources into the distribution network. While integrating local sources into the system, several challenges come up, like voltage fluctuations and losses, safety and stability, protection coordination, and mitigation strategies. From those problems, one of the problems is deciding ‘the right place with the right size’ for the local source in RSDS. This work proposes a modified pathfinder optimization algorithm that has a fast convergence rate and the best balance between exploration and mining ability compared to other methods and previous PFOs. Applying MPFO to the IEEE-12 and IEEE-33 test systems to find the optimal place and size of the local source with the help of VSI and LSF. Compare other traditional methods.

DRMF: Optimizing machine learning accuracy in IoT crop recommendation with domain rules and MissForest imputation

2024-07-12T04:38:21+00:00

In the realm of IoT-driven precision agriculture, addressing missing data is crucial for reliable crop recommendation systems. This paper proposes the Domain Rules and MissForest (DRMF) algorithm to handle the above mentioned challenge. The proposed DRMF algorithm was thoroughly tested on an IoT agriculture dataset with the introduction of a missingness mechanism in the form of MAR with 10 % of missing values. A comparison analysis with the usual imputation techniques such as Mean Imputation, kNN Imputation, Linear Regression, EM Algorithm, Multiple Imputation, and the standard MissForest was performed and the proposed method was found to perform better. The DRMF algorithm attained an unmatched Root Mean Squared Error (RMSE) value of 0.025 and a Mean Absolute Error (MAE) value of 0.012, displaying a significant superiority over its competitors. It is important to note that the algorithm also achieved a Mean Absolute Percentage Error (MAPE) of 5.0% and an R-squared value of 0.970, with the overall accuracy rate being 99.0%. The quantitative findings serve to emphasize the effectiveness of the DRMF algorithm in improving the prediction accuracy of crop recommendation models. The novelty of this research is in the combined approach that merges the computational power of the MissForest algorithm, and the insight offered by domain-specific agricultural rules.

A bigdata analytics method for social media behavioral analysis

2024-08-22T05:28:49+00:00

Twitter on web-based entertainment has become an important part of everyday life. This medium provides a list of current events in real time, most of which is difficult to understand, so it must be sorted to find useful information. Human biology, pharmacology, and experimental factors influence their behavior. Twitter tweets are a text store that can reflect human emotions and sentiments. Behavior Analytics (BA) is analyzing the behavior of individuals. BA can be used to filter useful information from tweets in healthcare and business applications. This paper presents the analysis of human behavior using Twitter data and a proposed Social Media Behavior Analysis Big Data Analytics (BASMBA) algorithm. The proposed algorithm uses several techniques in its preprocessing, feature selection, and classification of tweets using BIGDATA. Additionally, the accuracy of the algorithm is verified using the precision factor and recovery time.

An asymmetric key encryption and decryption model incorporating optimization techniques for enhanced security and efficiency

2024-08-13T05:50:23+00:00

In Wireless Sensor Networks (WSN), ensuring data security is crucial for maintaining the confidentiality and integrity of transmitted information. Asymmetric key encryption methods serve as fundamental tools in securing communication within WSNs. This paper introduces an innovative Asymmetric Key Encryption and Decryption Model, integrating optimization techniques to enhance security and efficiency in data transmission within WSNs. By incorporating optimization algorithms into key generation and encryption processes, the proposed model strengthens cryptographic key robustness and reinforces encryption mechanisms against potential threats. Leveraging advanced optimization methodologies like genetic algorithms, simulated annealing, or particle swarm optimization, the model optimizes key parameters to mitigate vulnerabilities and bolster resistance against brute force and cryptanalysis attacks. Additionally, the model streamlines encryption and decryption procedures, optimizing computational resources and reducing associated overheads. Through experimental validation and performance analysis, the effectiveness of the proposed model is demonstrated by achieving improved security, reduced computational complexity, and enhanced data transmission efficiency. This research contributes to advancing WSN security by offering a sophisticated and efficient solution for safeguarding sensitive information in digital communication networks.

A resilience framework for fault-tolerance in cloud-based microservice applications

2024-07-08T06:44:17+00:00

Cloud-distributed systems offer significant opportunities for fault-tolerant applications. Microservices have gained significant acceptance as a cloud-based architecture for building fault-tolerant cloud applications. The primary aim of this study is to develop a dependable resilience framework, incorporating appropriate design patterns, that can be applied to any cloud applications. This framework combines a bulkhead utilizing a little law approach and an auto-retry circuit breaker, which can be seen as a fault tolerance pattern. This will eliminate the need for manual setting of design patterns, resulting in maximum throughput, availability of resources and the performance can be increased up to 55.3% from the average execution duration.

MOHCOA: Multi-objective hermit crab optimization algorithm for feature selection in sentiment analysis of Covid-19 Twitter datasets

2024-08-07T06:33:08+00:00

The COVID-19 pandemic has led to a flood of data on Twitter, making it crucial to analyze public opinion. However, the large amount of data is challenging to manage. This paper presents the multi-objective hermit crab optimization algorithm (MOHCOA) to tackle this problem by improving the accuracy of sentiment analysis, selecting the best features, and reducing computing time. Inspired by how hermit crabs choose their shells, MOHCOA balances exploring new features and using known ones, which helps in better sentiment classification while cutting down on unnecessary data and processing time. Compared to other methods, MOHCOA is more efficient in selecting features and improving model accuracy. For the bag of words (BoW) set, MOHCOA narrowed features down to 2005, and for the BoW + COVID-19 keywords set, it chose 2278 features. When used with a random forest model, MOHCOA achieved a precision of 0.84, recall of 0.69, F1-score of 0.75, and accuracy of 0.83. This shows that MOHCOA is effective in managing large data sets, making it a useful tool for analyzing text and public sentiment during events like the COVID-19 pandemic.

Smart alerting services: Safeguarding women and children in the digital age

2024-08-20T11:13:30+00:00

In the digital age, safeguarding women and children has become increasingly challenging due to the pervasive nature of cyber threats and the rapidly evolving environments in which they live. This paper investigates the role of smart alerting services in enhancing the protection of these vulnerable groups. It focuses on the functionality of existing safety apps, including features like real-time location tracking, emergency alert systems, and geofencing. By analyzing various case studies and technologies, it highlights how these services address safety concerns, their effectiveness, and the challenges faced in their deployment. The research provides insights into how smart alerting systems contribute to personal safety and the potential for future advancements in this field.

Enhancing classification accuracy on code-mixed and imbalanced data using an adaptive deep autoencoder and XGBoost

2024-07-22T07:05:49+00:00

This study introduces a pioneering approach for enhancing classification accuracy on code-mixed and imbalanced data by integrating an adaptive deep autoencoder with dynamic sampling techniques. Targeting the intricate challenges of sentiment analysis within such datasets, this methodology employs an enhanced XGBoost classifier, optimized to leverage the nuanced features extracted by the autoencoder. The experimental evaluation across diverse datasets, predominantly involving Tamil-English code-mixed texts, demonstrates a notable improvement in performance metrics: accuracy reached 84.2%, precision was recorded at 74.8%, recall stood at 78.4%, and the F1-Score achieved 76.6%. This marks an enhancement over existing methods by 0.5% to 1.5%, substantiating the model's robust capability in effectively handling linguistic diversity and class imbalances. The novelty of this research lies in the seamless integration of dynamic sampling within the autoencoder's training loop, significantly boosting the adaptability and effectiveness of the machine-learning model in real-world applications.

Hybridization of bio-inspired algorithms with machine learning models for predicting the risk of type 2 diabetes mellitus

2024-08-22T05:57:42+00:00

Type 2 diabetes mellitus is a chronic condition that affects millions of people worldwide. Predicting the risk of developing this disease is critical for early intervention and prevention. Bio-inspired algorithms and machine learning models have shown promising results in predicting the risk of type 2 diabetes mellitus. In this paper, we will explore the use of these two approaches and their hybridization to improve the accuracy of risk prediction. The first section will introduce bio-inspired algorithms and their application in predicting the risk of type 2 diabetes mellitus. We will discuss the advantages of using these algorithms and their limitations. The second section will focus on machine learning models and their potential in predicting the risk of type 2 diabetes mellitus. We will also discuss the limitations of this approach. The final section will compare and contrast the two approaches and explore how their hybridization can overcome their limitations and improve the accuracy of risk prediction. Overall, this paper aims to provide an in-depth analysis of the use of bio-inspired algorithms and machine learning models in predicting the risk of type 2 diabetes mellitus and their hybridization to improve their accuracy.

Adoption of artificial intelligence and the internet of things in dental biomedical waste management

2024-07-03T06:10:27+00:00

The production of waste is an ongoing activity that must be managed efficiently to protect both the environment and the health of the general population. Therefore, proper management of waste from dental care is essential in protecting the environment's health, and it should become an inherent part of dental services. This study’s primary objective was to use artificial intelligence in dental biomedical waste management. The goal of this project was to develop an automated technique for categorizing dental trash to enhance the process of managing biological waste. In the proposed research, the Support Vector Machine classifier has been regarded as the most effective method of classification for a dataset of Euclidean size. The most effective classifier used in the model is a support vector machine (with an accuracy of 96.5%, 95.9% specificity, and 95.3% sensitivity) when classifying the different types of garbage. The categorization is accomplished through machine learning techniques, to accurately separate waste into recycling categories, precisely four categories for dental biomedical waste. Based on the findings of these trials, This method has the potential to be used for garbage sorting and classification on different scales, which might aid in the scientific disposal of biological waste.

A PPR-based energy-efficient VM consolidation in cloud computing

2024-06-13T15:15:33+00:00

The tendency to do more jobs while consuming less energy is crucial to energy efficiency in the cloud environment. To use less energy while performing more tasks at the best throughput, this study provides an energy-efficient technique (PPR_DWMMT_1.1) for VM consolidation in a cloud domain. Our approach uses the PPR to determine the upper threshold for overload detection and the lower threshold for underload detection. Additionally, PPR_DWMMT_1.1 considers the overall workload utilisation of the data centre when selecting a lower threshold, which could reduce VM migrations. Our proposed method, PPR DWMMT 1.1, is compared to the simulation results of the four reference techniques, IQR_MMT_1.5, LR_MC_1.2, MAD_MU_2.5, and THR_RS_0.8. Our solution has been demonstrated to use less energy, trigger fewer host shutdowns and live migrations, and achieve the best performance when compared to the other four approaches.

A novel and an effective intrusion detection system using machine learning techniques

2024-08-22T05:44:15+00:00

Network environments become more and more diverse with the presence of many different network protocols, services, applications and so on. With this diversity, many different types of attacks appear and target at a computer or a network every day. A single type of intrusion detection systems (IDSs), which has its own advantages and disadvantages, seems to be insufficient to detect all the attacks. Since us don’t know which types of attacks are coming next, the primary difficulty lays on selecting of the best IDS at a certain time. In our scenario, we assume that each IDS has its own favorite types of attacks to detect. In this paper is investigated for intrusion detection system (IDS) and its performance has been evaluated on the normal and abnormal intrusion datasets (KDDCUP99). New technique of k-NN algorithm using NA (Network Anomaly) rules for intrusion detection system is experimented. The research work compares accuracy, detection rate, false alarm rate and accuracy of other attacks under different proportion of normal information. Comparison between Naive Bayes classifier, SVM and NA-kNN for same training data set and testing data set has carried out. Experimental results show that for Probe, U2R, and R2L, NA-kNN gives better result. Overall correct count to detect correct attacks is larger in NA-kNN than other classifier algorithms.

Comparative study of the foundation model of a 220 kV transmission line tower with different footing steps - Finite element analysis

2024-08-02T05:23:16+00:00

Transmission line Towers are structures commonly used to support the phase conductors and shield wires of a transmission line. The present work describes the analysis of superstructure and substructure of a 220kV transmission line tower. The tower is a self supporting three dimensional type and designed for a height of 33.25 meters which is usual height of supporting conductors to transmit power one point to another in Andhra Pradesh. Super Structure of the transmission line tower has been analysed considering wind loads as per codal provisions IS 802:2002. Reactions obtained from the results in each leg of a transmission line tower at base have been considered as forces for the Finite Element analysis of substructure system. The analysis has been carried out using Ansys Workbench by considering Finite Element Analysis concept with Solid 65 as element for concrete foot steps and truss element for steel sections. Various parameters like deformation & Stresses are observed in the stub angle section and foundation system with five footing steps to study the compare the results between different foot steps of a foundation model. The numerical analysis such as finite element method has enabled the prediction of stresses of foundation of Transmission line Tower.

BTEDD: Block-level tokens for efficient data deduplication in public cloud infrastructures

2024-06-03T19:10:19+00:00

In today's digital era, the exponential growth of data necessitates effective storage and management solutions. The cloud has vast storage possibilities to store huge amounts of data. Public access to the cloud leads to duplicate copies of data stored in the storage. Maintaining a single copy of data in the cloud is most important for efficient data storage management. This paper introduces a groundbreaking strategy for improving the efficacy of cloud storage through innovative data deduplication techniques at the block levels. The block-level duplication verification efficiently identifies the duplicate data in the storage. It helps to protect the duplicate storage in the cloud data storage infrastructure. The block-level deduplication technique uses variable-length blocks based on the duplicate content of the block. Initially, A file is divided into a number of blocks with a size of 5kb. According to the proposed method, If any block is partially matched with a block already stored in the cloud, then that block is further divided into smaller blocks based on the matching percentage. The smaller blocks help to deduplicate the data more effectively. The work is implemented in a live cloud setting with a C# application hosted on MyASP.NET. The proposed methodology's effectiveness is validated against existing deduplication techniques. The results reveal a marked improvement in storage utilization and data management, affirming the potential of the approach to revolutionize cloud storage efficiency.

An optimized real-time human detected keyframe extraction algorithm (HDKFE) based on faster R-CNN

2024-08-09T14:54:12+00:00

The aim of this project is to support criminal investigators by utilizing surveillance camera footage in their investigations. To apprehend the culprit, it is necessary to examine the video footage and extract the relevant and crucial information. Analyzing lengthier videos might provide challenges due to the time needed to process the entire video while maintaining its semantic features. In this situation, a dataset is collected in real-time to aid in the criminal investigation, which consequently requires the use of keyframes. The study illustrates that Content-based video retrieval (CBVR) enables the video analysis technique. Keyframe extraction is a significant component of video analysis. The main objective of key frame extraction is to reduce the amount of repetitive frames in a video, thereby improving the clarity and efficiency of the scenario. Moreover, it optimizes video sequences to expedite processing. The study paper introduces the Human Detected Keyframe Extraction algorithm (HDKFE), which utilizes a dual-stage methodological approach. The Faster Region-Convolutional Neural Network (Faster R-CNN) detects humans in surveillance by identifying frames that contain humans and reporting them using an optimized threshold value. The frames then identify a suitable keyframe by recognizing local maxima through the absolute difference between frames in the subsequent phase. This significantly decreases the complexity of long-term criminal investigations. The experimental report reveals that the HDKFE approach achieves a precision of 98.87% while minimizing both space and time complexity.

An improved social media behavioral analysis using deep learning techniques

2024-08-22T05:34:56+00:00

Most online users share their opinions and comments or give their valuable feedbacks on a variety of subjects. Public opinions and comments in social media have had great impact on social and political systems. This vast information can be reviewed and analyzed. As this online information grows in numbers it requires efficient processing. Thus, this information can be mined or analyzed effectively, making it a suitable candidate for data mining. Twitter’s micro blogging service has more than 250 million active users who post short messages about any topic. This vast information is a meaningful source of information regarding different aspects of. This paper proposes to mine and extract information from tweets called IBADL (Improved Behavioral Analysis using Deep Learning), the goal of the proposed technique is to mine information through the study of the tweets posted and conduct an analysis for drawing meaningful conclusions about the behavior of Twitter users.

ETTG: Enhanced token and tag generation for authenticating users and deduplicating data stored in public cloud storage

2024-06-27T19:16:11+00:00

As cloud storage services continue to grow in popularity, the need for secure and efficient data management has become paramount. Public cloud storage offers benefits such as cost efficiency, scalability, and accessibility, but it also presents significant challenges related to data security and storage optimization. To address these challenges, the paper proposes an Enhanced Token and Tag Generation (ETTG) technique designed to improve data deduplication in public cloud storage. ETTG utilizes advanced cryptographic methods to generate secure tokens and tags, ensuring robust, efficient deduplication processes. The comprehensive evaluation demonstrates that ETTG significantly reduces computation time compared to existing techniques, making it particularly suitable for data-intensive cloud environments. By minimizing redundant data and enhancing data security, ETTG not only optimizes storage utilization but also improves overall system performance. This paper details the design and implementation of ETTG, its evaluation against existing methods, and its potential impact on the efficiency and security of cloud storage services.

A secured routing algorithm for cluster-based networks, integrating trust-aware authentication mechanisms for energy-efficient and efficient data delivery

2024-08-13T05:52:53+00:00

Secure routing in Wireless Sensor Networks (WSNs) is vital for preserving data veracity and privacy in the face of possible threats. Traditional routing protocols lack robust security mechanisms, making WSNs vulnerable to attacks. Secure routing protocols in WSNs aim to address these vulnerabilities by implementing authentication, encryption, and intrusion detection techniques to ensure secure and reliable data transmission while minimizing energy consumption. This paper proposes a novel secured routing algorithm tailored for cluster-based networks, aimed at enhancing energy efficiency and data delivery security by integrating trust-based authentication mechanisms. The approach begins with the design of a clustering algorithm, which organizes network nodes into clusters based on proximity or network topology. Subsequently, a trust-based authentication mechanism is developed to evaluate the reliability and integrity of both nodes and links within the network. Building upon these foundational elements, a secured routing protocol is devised to capitalize on the cluster-based organization and trust-based authentication, thereby facilitating energy-efficient and secure data transmission. The proposed algorithm and authentication mechanism Cluster and Optimal Routing Assisted Cryptograph (CORAC) are implemented within a simulated network environment to validate their efficacy. Performance evaluation is conducted through simulation studies, focusing on key metrics such as packet delivery ratio, energy consumption, and security effectiveness. This comprehensive approach aims to address the dual challenges of energy efficiency and data security in cluster-based networks, offering a promising solution for future deployments in various applications.

RPL-eSOA: Enhancing IoT network sustainability with RPL and enhanced sandpiper optimization algorithm

2024-08-08T10:33:53+00:00

The internet of things (IoT) encompasses extensive networks of interconnected devices, playing a crucial role in various applications. However, managing these networks presents significant challenges, particularly in cluster head selection, which is critical for energy efficiency and sustainability. To eradicate these challenges, this paper combines the capability of routing protocol for low-power and lossy networks (RPL) with an enhanced sandpiper optimization algorithm (e-SOA) to dynamically optimize network configurations. This combination, termed RPL-eSOA, improves energy management and extends network longevity while maintaining robust communication pathways. Through simulation and comparative analysis, RPL-eSOA demonstrates superior performance in enhancing network lifetime and operational efficiency compared to traditional methods. It achieved a 100% packet delivery ratio (PDR) and significantly reduced latency to 475 ms.

Crop yield prediction in diverse environmental conditions using ensemble learning

2024-08-28T13:26:01+00:00

Precise assessment of crop yield is a vital component in agricultural planning and decision-making, having immediate consequences for food security and allocation of resources. This study presents a new approach for predicting agricultural output in different climatic conditions by integrating the xgboost algorithm with the Whale Optimization Algorithm (WOA). XGBoost is a kind of ensemble learning method that enhances the accuracy of predictions by combining the results of many weak learners. However, the performance of the system may be significantly affected by the selection of suitable hyper parameters and feature subsets. To address this problem, we use the WOA algorithm, a nature-inspired optimization approach that mimics the foraging behavior of humpback whales. This technique is used to improve the parameters of xgboost and discover the most influential features. We evaluate the proposed model by using extensive datasets that include a diverse array of crops, soil compositions, climatic conditions, and geographic regions. The results suggest that the xgboost-WOA model outperforms traditional machine learning models in terms of both projected accuracy and efficiency. Furthermore, the suggested method showcases robust and reliable performance across different environmental circumstances, highlighting its potential for practical use in precision agriculture. This research emphasizes the effectiveness of combining AdaBoost with WOA for forecasting agricultural output. Furthermore, it contributes to the development of advanced predictive systems to support sustainable agricultural operations in adapting to climate variations and changing environmental conditions.

Analysis of substrate materials for flexible and wearable MIMO antenna for wireless communication

2024-05-30T16:24:44+00:00

In recent years, flexible and portable antenna technologies have become critical to the development of next-generation wireless communication technologies such as 5G and beyond. The purpose of this study is to evaluate the performance of three basic materials used in the design of flexible and portable antennas - FR4, PVC and LCP. The methodology involves studying the resonant frequency ranges, return losses, bandwidth, gain and antenna radiation efficiency of each material. The results show that LCP has the widest bandwidth and highest efficiency, making it suitable for high frequency applications. Substrate PVC, while limiting significant bandwidth, limits high frequency accuracy due to its higher dielectric constant. Although FR4 is cost-effective, its effectiveness is limited in high-frequency applications due to its narrower bandwidth and higher loss coefficient. These results indicate that LCP is an optimal choice for advanced RF applications, especially in next-generation wireless communication technologies. Future research should focus on improving the properties of these materials to further improve their suitability for flexible and portable antennas.

Adoption of health information systems in emerging economies: Evidence from Ghana

2024-07-04T06:16:15+00:00

This research aims to assess the implementation of health information systems (HIS) in state-owned hospitals in Ghana, particularly focusing on teaching, regional, district, and quasi-government hospitals. The purpose is to evaluate the HIS application, training, data protection measures, internal system communication within hospitals, and the impact of internet connectivity and electricity supply on HIS adoption. The study employed a quantitative research design. Data were collected through questionnaires from 80 healthcare workers across 10 hospitals in Northern, Middle, and Southern regions. Quantitative data was analyzed using frequencies and percentages. The research revealed that although some hospitals had implemented HIS, there was inadequate training for healthcare workers. While data protection measures were in place, challenges included limited internal system communication, hindering effective HIS operation within hospitals. Additionally, poor internet connectivity and electricity supply hindered HIS usage and adoption. This study contributes by uncovering specific challenges in HIS implementation within Ghanaian hospitals, emphasizing the need for enhanced training, internal system communication, and addressing infrastructure limitations.

Hyperparameter tuning of diabetes prediction using machine learning algorithm with pelican optimization algorithm

2024-08-07T03:01:37+00:00

Machine learning algorithms are employed in public health to forecast or diagnose chronic epidemiological illnesses like diabetes, which have global rates of transmission and infection. Machine learning technology may be applied to diagnostic, prognostic, and evaluation methods for a number of illnesses, including diabetes. This work presents a novel approach based on a novel metaheuristic optimization algorithm to improve diabetes categorization. 738 records were included in the final analysis of the main data, which was acquired in 2013 in accordance with the security protocols specified in the Declaration of Helsinki. This approach suggests a novel feature selection technique based on DBERDTO (Douche Optimization technique) and the dynamic Al-Biruni earth radius. A random forest classifier was used to categorize the chosen features, and the suggested DBERDTO was utilized to optimize the parameters. In this work, we investigate hyperparameter tuning for improved diabetes case prediction using the Pelican Optimization Algorithm (POA) in conjunction with the XGBoost machine learning technique. To prove the effectiveness and superiority of the suggested approach, it is tested against the most recent machine learning models and optimization techniques. The method's overall accuracy for classifying diabetes was 99.65%. These test results attest to the suggested method's superiority over alternative categorization and optimization techniques.

Classification of glaucoma in retinal fundus images using integrated YOLO-V8 and deep CNN

2024-07-17T02:25:46+00:00

To propose a new system to identify glaucoma at an early stage with the help of deep learning-based AI method by utilizing Retinal Fundus Images (RFI). The method detects intrinsic key structures in the fundus images to predict retinal nerve layer thickness in order to improve the accuracy of glaucoma detection and classification. To learn complex and hierarchical image representations, the CNN model is used to identify the continuous value of retinal nerve layer thickness from RFI. The Binary Cross Entropy (BCE) loss function is used to perform multi-classification tasks to discover classes such as healthy eye, eye with glaucoma, and glaucoma suspect. In order to identify the local and global features in RFI, the YOLO-V8 object detection method is employed, which also helps to perform image localization, which includes image segmentation, deep optic disc analysis, and the extraction of ROIs. The main focus is given, especially for RNL thickness around OD regions and CDR measurement to perform glaucoma identification tasks. The PAPILA dataset is utilized with the ophthalmology records from 244 patients and includes 488 digital retinal fundus images, covering both left and right eyes for both male and female categories. The CNN model is trained on the PAPILA dataset with labeled RNL thickness values. The performance of CNN-BCE with YOLO-V8 is evaluated using MATLAB and compared against the prevailing approaches such as SVM, ADABOOST, and CNN-Softmax classifiers. The new model outperforms the existing methods with proven results of 98.88% accuracy rate, 0.9 dice-score, 97.74% and 98.03% sensitivity & specificity, 98.6% and 98.78% precision & recall, 98.06% f-score, and 0.92 true positive rates and 0.10 false positive rates under AUC-ROC. This clearly shows that the newly proposed CNN-BCE with YOLO-V8 detects and classifies glaucoma, which helps ophthalmologists perform potential screening and predict better treatments.

Novel deep learning assisted plant leaf classification system using optimized threshold-based CNN

2024-08-22T05:47:28+00:00

In general, plant classification systems can be a beneficial tool in agriculture, especially when identifying plant types in a systematic what's more, sensible way. Already, plant breeders relied on observation and experienced personnel to distinguish plant varieties. However, some plants, such as leaves and branches, have nearly identical characteristics, making identification difficult. Therefore, there is a need for a system that can solve this problem. Therefore, this study focuses on the characterization of plant leaves using convolution neural network (CNN) techniques. The main idea of this paper is to propose a new deep learning-based model for plant leaf classification. Initially, preprocessing is done using RGB-to-grayscale conversion, histogram equalization, and median filtering to improve the image quality required for further processing. The results show that with the activation layer of the algorithm, 15-layer network design and a trial-training ratio of 70-30, the plant leaf classification system can achieve 90% classification accuracy of coriander and parsley with an error rate of 0.1. Furthermore, due to its high accuracy, the system can be extended to other uses such as identifying plant diseases and species.

IoT based energy aware local approximated MapReduce fuzzy clustering for smart healthcare data transmission

2024-07-02T07:02:28+00:00

Big Data is a collection of large amount used to store and to process for future use. Internet of Things (IoT) technology is used in smart home, smart healthcare. IoT has limited resources like processing capability and supplied energy. Many researchers carried out their research on resource optimized data clustering in bigdata environment. But, the computational complexity and energy consumption was not reduced by existing techniques. Therefore, IoT based Energy Aware Local Approximated Fuzzy MapReduce Clustering (IoT-EALAFMRC) Method is introduced. The main objective of IoT-EALAFMRC Method is introduced to perform an efficient priority based data transmission in smart healthcare environment. Initially, IoT devices are used to collect the large number of patient data in different location at a same time. During data transmission, there is a chance of traffic occurrence. In order to reduce the traffic occurrence rate during the data transmission to the physician (i.e., doctor), Energy Aware Local Approximated Fuzzy MapReduce Clustering is used with map and reduce function to group the patient data into normal constrained data or emergency constrained data based on physical health condition with higher clustering accuracy. IoT-EALAFMRC Method performs the cluster assignment based on neighborhood relationships among data. After clustering of patient data, the data is sent to the physician with minimum time consumption. Through minimizing the traffic, retransmission of patient data gets reduced. This in turn helps to reduce the energy consumption. Experimental evaluation is carried out using IoT-EALAFMRC Method on factors such as energy consumption, clustering accuracy and execution time for different number of patient data.

Innovative technological advancements in solving real quadratic equations: Pioneering the frontier of mathematical innovation

2024-08-02T05:18:12+00:00

The advancement of computational methodologies in solving real quadratic equations has emerged as a focal point in contemporary mathematical research. This study explores the efficacy of innovative technological tools and interdisciplinary collaboration in revolutionizing quadratic equation solutions. By integrating symbolic computation systems such as Mathematica and MATLAB with numerical libraries like NumPy and SciPy, alongside specialized software frameworks, researchers have unlocked new avenues for precise and efficient quadratic equation solving. Symbolic manipulation techniques, including factoring, completing the square, and utilizing the quadratic formula, provide closed-form solutions, offering a direct approach to solving quadratic equations. Numerical root-finding algorithms, such as Newton's method and the bisection method, along with iterative techniques like fixed-point iteration, contribute to approximating solutions iteratively, enhancing solution accuracy and convergence rates. Real-world quadratic equations from diverse domains, including physics, engineering, economics, and optimization problems, serve as test cases to evaluate the performance of computational methodologies. Performance evaluation criteria encompass accuracy, convergence rate, computational efficiency, and robustness, ensuring the reliability of computational solutions. Statistical analysis and validation techniques validate the accuracy and reliability of solutions against analytical solutions and established mathematical software packages. Interdisciplinary collaboration between mathematics and computer science drives innovation, pushing the frontier of quadratic equation solving.

Tweaking of the morphological pattern in copper sulphide nanoparticles: How does it affect the optical properties?

2024-05-31T07:22:30+00:00

A simple wet chemical method is employed to synthesize covellite (CuS) nanostructures and different nanostructures are synthesized by varying molar ratios of copper acetate monohydrate to sodium thiosulfate pentahydrate in the starting solution. Microstructural characterizations confirm the excellent quality of synthesized CuS nanostructures having different morphology. The absorption spectra and morphology of synthesized samples change significantly with changing experimental conditions. The intensity ratio of NIR peak to UV peak, NIR peak position, and FWHM of NIR peak vary notably with the molar ratio of copper acetate monohydrate to sodium thiosulfate pentahydrate in the initial solution. The facile technique adopted in this work opens up an easy way to prepare CuS nanostructures with outstanding intensity and FWHM of NIR peak due to their unique optical properties.

Stochastic artificial neural network for magdm problem solving in intuitionistic fuzzy environment

2024-08-13T04:22:04+00:00

In this work, we have presented the decision-making models based on ANN, which takes argument pairs, of the intuitionistic fuzzy values and defuzzifies the decision matrices and create Stochastic matrices for producing input for computations of ANN. Concepts from Stochastic processes namely Markov chains and limiting distributions are discussed in detail in this research work and has been applied for effective decision making in complex situations. The numerical illustration provided in this work will be solved using the Markov chain models and some linear space techniques and applied in Artificial Neural Network (ANN). A new Algorithm is also developed for solving the MAGDM problems applying the proposed methods. The Numerical illustrations are solved with defuzzyfication operators and the results are recorded for effectiveness and comparisons are made with some existing methods. The new method proves to be more effective than the previous methods of ANN for MAGDM problems

An optimal fuzzy inventory model for rice farming using lagrangean method

2024-08-13T04:11:49+00:00

Rice is the staple diet for millions of people in Asia and around the globe. Nowadays, Farmers are facing many challenges in the field because the soil’s fertility is declining, it is growing harder for farmers to cultivate their land. Numerous elements, such as soil erosion, salinity, Poor Nutrient Management and temperature variations, have an impact on soil fertility. Rainwater runs swiftly across upland soils, making it difficult for farmers to hold on to the moisture in the soil. Now, it is time to rethink the cropping patterns based on agroclimatic zones. India is the leading producer of Rice crops. It is one of the major food crops that provide nourishment for millions of people every day. This paper aims to investigate the fuzzy production-related factors for one acre of rice farming. Various costs are fuzzified as trapezoidal fuzzy numbers and deffuzzified by using the beta distribution method. This proposed model is to determine the optimal solution using lagrangean method. A numerical example is concluded.

Amelioration of the UV-blocking property of ZnO nanoparticles as an active sunscreen ingredient

2024-06-01T17:38:30+00:00

ZnO nanoparticles are synthesized employing simple wet chemical methods and different samples are prepared by varying liquid mediums (consecutively water medium, ethanol medium, methanol medium) in the initial solution. All samples are evaluated for optical, microstructural, and compositional characteristics. The UV-vis spectra of ZnO nanoparticles show interesting and remarkable changes with varying synthesis conditions. The fascinating observation of generating high %UVA-blocking and %UVB-blocking can be attributed to getting control over the UV-vis spectra of synthesized ZnO nanoparticles. These NPs can provide full protection from total UV region due to their high %UVA-blocking and %UVB-blocking values. When the experimental settings are changed (from water medium to ethanol medium then to methanol), the % UVA-blocking and % UVB-blocking is highest for the sample synthesized in methanol medium, lower for the sample synthesized in ethanol medium and lowest for the sample synthesized in water medium. The facile technique adopted in this work opens up an easy way to synthesize ZnO nanoparticles (NPs) with a broad absorption spectrum in the UV area and can provide full protection from the whole UV range.

Neural net influenced magdm problem with modified choquet integral aggregation operators and correlation coefficient for triangular fuzzy intuitionistic fuzzy sets

2024-08-13T04:46:17+00:00

With respect to Multiple Attribute Group Decision Making (MAGDM) problems in which attribute values take the form of Triangular Fuzzy Intuitionistic Fuzzy Set (TrFIFS) values, a new decision-making analysis method is developed. First, a novel correlation coefficient for the TrFIFS is proposed and then utilized in the decision-making process for the ranking of the best alternatives. The new correlation coefficient is substantiated by several theorems proved to establish its effectiveness. Then, two TrFIFS Choquet integral aggregation operators are developed and utilized in solving the MAGDM problem. The Triangular Fuzzy Intuitionistic Fuzzy Improved Choquet Integral Averaging (TrFIFIMCOA) operator for TrFIFS and Triangular Fuzzy Intuitionistic Fuzzy Improved Choquet Integral Geometric (TrFIFIMCOG) operator for TrFIFS are proposed and some desirable properties are studied. The prominent characteristic of the operators is that they can not only consider the importance of the elements or their ordered positions, but also reflect the correlation among the elements or their ordered positions. Using the proposed two operators, the input vector is produced for Artificial Neural Network (ANN) which is solved to provide an effective solution for MAGDM problem. The newly proposed correlation coefficient and the aggregation operators are effectively utilized to solve real life decision problems. Finally, an illustrative example has been given to show the developed method and comparisons are made with existing methods.

Multi-fuzzy set similarity measures using S and T operations

2024-08-19T17:45:49+00:00

This paper introduces some similarity measures on multi-fuzzy set, enhancing multi-fuzzy set analysis through a weighting mechanism using S and T operations. Using a fuzzy matrix, we define a weighted relation, summarizing the characteristics of multi-fuzzy set M in relation to a weighted column matrix A. The relation A≤is established by comparing membership grades and weighted elements, expressing similarity or dissimilarity within elements of X.

Fuzzy inventory model with warehouse limits and carbon emission

2024-08-13T04:16:26+00:00

The theory of optimization deals with some techniques to find optimal solutions for a given mathematical problem. This paper examines the use of the Yager ranking method to minimize the total cost in an EOQ. The assortment of goods and different costs with demand must be balanced for an inventory management system to operate without any issues. The sustainable approach takes carbon emissions to be considered with constraints on warehousing capacity. The order lot quantity is not allowed to exceed the capacity limit available warehouse. The parameters are fuzzified by using a trapezoidal fuzzy number. The Yager ranking method is used to find the total cost. Finally numerical example is carried out.

Existence of a homeomorphism from the space of continuous functions to the space of compact Subsets of a topological space, X

2024-08-02T05:30:24+00:00

This paper presents proof that there exists a subspace of the space of continuous functions on a topological space X, which is homeomorphic to the space of compact subsets of X. Those let C(X) denote the space of continuous functions on a topological space X and K(X) be the space of compact subsets of X. We prove that there exists a subspace of C(K(X)) which is homeomorphic to C(X). The result remains valid for compact open topology and point-wise convergence topology on K(X).

Assessment of heavy metal contamination in Trifolium alexandrium and Spinacia oleracea using ICP-MS: A comparative analysis across different districts in eastern Uttar Pradesh

2024-08-13T18:02:26+00:00

In areas near sugar mills, many plants have been found to accumulate unsafe heavy metals such as arsenic (As), cadmium (Cd), lead (Pb), nickel (Ni), and zinc (Zn), which can pose severe threats to human and environmental health. The objective of this study is to assess the level of heavy metal concentrations in different tissues of Trifolium alexandrinum and Spinacia oleracea samples collected from Ayodhya, Bahraich, and Gonda districts in Uttar Pradesh, India. Heavy metal concentrations in plant samples were estimated using inductively coupled plasma mass spectrometry (ICP-MS). The present study revealed the variations in heavy metal distributions among plant species of different areas, with notably high levels of heavy metals in samples like T. alexandrinum, and S. oleracea collected near sugar mills. The differences observed within specific regions suggest that regional factors, such as soil and land use, influence the accumulation of heavy metals. These findings underscore the urgent need for continuous monitoring and control of heavy metal pollution to mitigate potential health and environmental risks associated with proximity to sugar mills.

Positive impact of using α-Fe2O3 nanoparticles as dietary supplements on some hematological parameters of an economically important minor carp Labeo bata (Hamilton, 1822)

2024-06-03T04:29:27+00:00

A simple, low-cost wet chemical method is employed to synthesize α-Fe2O3 nanoparticles (NPs) and different samples are synthesized by varying the calcination temperature. Microstructural characterizations confirm the excellent quality of synthesized α-Fe2O3 NPs having different sizes. The synthesized NPs are used as dietary supplements of an economically important minor carp Labeo bata (F. Hamilton, 1822), to investigate the effects on some hematological parameters (hemoglobin, red blood corpuscle, and hematocrit) of the fish. Significant improvements in hemoglobin (Hb), red blood corpuscle (RBC), and hematocrit (Hct), are observed owing to the treatment with α-Fe2O3 NPs. Increasing Hb, RBC, and Hct can be associated with the increased absorption of iron in its nano form into the fish body via dietary supplements. Our data further demonstrate that the hematological effect of L. bata becomes more favorable as the concentration of NPs rises and or the size of the NPs falls, up to a certain level.

The potential impression of fructo-oligosaccharides and zinc oxide nano composite against nicotine influenced cardiovascular changes

2024-07-31T02:10:23+00:00

Nicotine is easily transported to heart through blood and it increase the risk of coronary artery diseases by twofold besides developing a hyper-coagulable state (through platelets aggregation or fat deposition in artery), increased heart rate and blood, free radicals’ generation, increased cardiac stress, etc. Nutritional therapy establishes nutrients’ health potential without side effects. Fructo-oligosaccharides (functional soluble fiber) are capable of producing a higher amount of short-chain fatty acid (SCFA: fermented product) in the GI tract than other fibers. Zinc is an abundant metal for physiological and biological activities. A new fructo-oligosaccharides coated zinc nanocomposite (ZnOFNC) applied against nicotine tempted heart abnormalities. This scientific study was needed to establish a new nanocomposite, ZnOFNC (FOS coted zinc oxide nanocomposite), applied against nicotine entice heart abnormalities. ZnONPs and ZnOFNC synthesized through the green synthesis method were applied on an animal model. Required animals were (male albino rats, 100–120 gm bw) divided into six groups on the basis of toxic drugs (Nicotine) and therapeutic drugs (ZnOFNC, FOS, ZnONPs and vitamin C) treated for 15 days. The estimated parameters were biochemical parameters, electrocardiogram, platelets count and histological study. All the parameters of the ZnOFNC applied group result were satisfied with the control group than FOS, ZnONPs and vitamin C applied groups. Our result led us to conclude that the ZnOFNC may have the anti-nicotinic capability to protect the heart from nicotinic toxicity.

Assessing the impact of indoor air pollution on respiratory health: A survey of home residents in rural area

2024-08-21T14:52:59+00:00

Particularly in residential settings people spend more time inside a house. It is a serious environmental health hazard. The purpose of this research is to evaluate how house occupants' respiratory health is affected by indoor air pollution. The study focuses on typical indoor contaminants, nitrogen dioxide (NO₂), carbon monoxide (CO), particulate matter (PM), and volatile organic compounds (VOCs). The research uses a survey-oriented approach to gather information from inhabitants in various housing situations, such as urban, suburban, and rural regions. The survey addresses the prevalence of respiratory symptoms and conditions (such as allergies, asthma, and chronic obstructive pulmonary disease). The presence of indoor pollution sources (such as cooking stoves, tobacco smoke, and chemical cleaners), residents' awareness and attitudes towards air quality. The gathered information is examined to find any relationships between the reported respiratory health problems and the amounts of indoor pollution. The research results shown that, there is a direct correlation between indoor pollution levels and the frequency of respiratory complaints. The research emphasises the importance of using air purifiers, better ventilation, and public education on reducing indoor pollution sources. According to the findings, improving indoor air quality is crucial for home occupants' respiratory health and general well-being.

Community based seasonally water quality testing of tributaries of Dehradun

2024-07-25T10:05:37+00:00

The objective of the research was to assess the water quality of the Suswa river. Nine locations from the Suswa river of Dehradun were chosen for sampling. The research was carried out between 2020 and 2021. Physical and chemical parameters were analyzed by using different instruments. Turbidity (2-115 NTU), total dissolved solids (30-276 mg L-1), pH (7.58– 7.88), dissolved oxygen (6.04–10.32 mg L-1), hardness (124–198 mg L-1), alkalinity (40–84.6 mg L-1), nitrate (0.058–0.115 mg L-1), and phosphate (0.015–0.080 mg L-1) were among the significant parameters that were measured. The Suswa river water requires precautionary measures before use in order to prevent adverse health impacts on humans, as evidenced by the analysis of water samples obtained from several study locations around the study region. As a result, we need to keep an eye on the resources. Monitoring of the river’s geomorphic, environmental, and climatic changes should be done more often, and the results should be made public.