bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine learning model for identification of frontend and backend repositories in Github
Authors: Shakikhanli Ulvi , Bilicki Vilmos
Published: September 2023

Frontend and Backend repositories are the main components of the multi-repository structure. The developers or team managers upfront mostly do the identification of these repositories, but in Github, there is almost no tool or third party identification to identify these repositories. Since Github is the largest open source platform and is used by, many researchers and identification of these two repositories are crucial. This paper shows how this problem can be solved by building a machine learning model based on the file structure of the repository. The model has proven to be very successful with an accuracy of over 90% and can be used not only to identify the repository type, but also to identify the development language and environment.
EEG-Based brain-machine interface for categorizing cognitive sentimental emotions
Authors: Jaison Feon , Inzimam  , Bhardwaj Shambhu
Published: September 2023

The development of brain-machine interfaces (BMIs) has revolutionized the study of neuroscience by making it possible for the brain to communicate directly with outside objects. In this study, EEG brainwave data is used to categorize emotional experiences using both individual and group methods. We employ a four-electrode resolution (TP9, AF7, AF8, and TP10) commercial MUSE EEG headband. Film clips with clear emotional content elicit both good and negative emotions. For one minute per session, neutral resting data is also obtained without external stimuli. To do this, we use machine learning algorithms to decode and interpret participant EEG data as they perform activities that evoke a range of emotional reactions. Relevant characteristics are collected from the EEG signals using intensive data preprocessing, feature extraction, and selection approaches to identify the underlying patterns of cognitive sentimental feelings. Following that, the development and assessment of classification models, such as Gradient Artificial Neural Networks (G-ANN), use the retrieved features as input. In conclusion, this study presents an EEG-based BMI system for categorizing cognitive sentimental emotions. The proposed G-ANN achieves a high accuracy of 98.59%, demonstrating superior performance compared to existing methodologies.
Deployment of the deep learning fusion method to emotional semantic evaluation of natural language
Authors: Khan Gulista , Mandal Sanjeev Kumar , Sharma Sunil
Published: September 2023

The emotional semantic evaluation of natural language plays a crucial role in sentiment analysis. Deep learning methods have shown great potential in capturing the complex relationships between words and emotions. This paper proposes a deep learning fusion method for deploying emotional semantic evaluation. The technique combines multiple deep learning architectures to capture local and global contextual information, including Bidirectional Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and Self-Attention mechanisms. Pretrained GloVe word embedding’s utilized to enhance word representation. A novel fusion layer combines the outputs of individual models; employing self-attention means to assign weights dynamically. This allows the model to weigh the importance of different representations in the final prediction. Benchmark movie review (MR) for sentiment analysis and emotion classification tasks are used to evaluate the proposed method. Experimental results demonstrate superior performance compared to individual deep learning models and traditional feature-based approaches. The proposed fusion method effectively captures the nuances of emotional semantics in natural language, leading to more accurate and nuanced evaluations.
Analysis of psychological biometric data for gender identification using fusion features and deep learning
Authors: Chakravarty Ajay , R. Murugan , Srivastava Manish
Published: September 2023

A person's gender must be correctly identified for many applications, such as tailored services, social sciences, and human-computer interaction, to function properly. Traditional gender identification techniques mostly depend on physical traits, but new developments in psychological biometrics provide interesting alternatives. Over the last several years, several access control systems have included biometric security technologies to increase security. The handwritten signature is the psychological biometric characteristic that is most often used to validate daily documents like letters, contracts, wills, MOUs, etc. This study proposes an innovative Deep Learning (DL) approach to identify a person's gender from an image of their handwritten signature. The fusion of statistical and textural information taken from the trademark photos serves as the foundation for the proposed work. The texture is represented by the Pyramid Histogram of Oriented Gradients (PHOG) features. A novel sequence labelling multidimensional recurrent neural network (SLMRNN) is employed to classify the writer's gender. Extensive experiments are carried out on the gathered dataset to assess the performance of the suggested technique. The efficacy of the fusion features and DL models for gender recognition is evaluated using a variety of measures, including accuracy, precision, recall, and F1 score. To evaluate the suggested approach against existing procedures and emphasize its advantages, if any, comparative assessments are also carried out. The findings show that, in comparison to conventional approaches, the combination of behavioral biometric variables with cutting-edge DL algorithms greatly enhances gender identification accuracy. The suggested approach is anticipated to be beneficial in the development of effective computer vision tools for forensic analysis and authentication of papers with handwritten signatures.
Forecasting diseases that affect plant leaves and moisture levels in the soil using a data mining approach
Authors: Shree Ritu , Gupta Rupal , Srikanth V.
Published: September 2023

The foundation of the global and Indian economies is agriculture. Since agriculture started millions of years ago, many environments, civilizations, and technical developments have fostered and defined the evolution of agricultural technology. In this study, we examine how we may analyze images of plants and soil to better keep tabs on their health, as well as how we can determine how much water each kind of plant needs. Images of the plants and soil are first taken using a digital camera with the necessary resolution. The form and geometric characteristics are extracted from the plant images using the inner distance shape context-based descriptor and geometrical descriptors. The soil images are also used to extract features and color properties. The botanical plant species dictionary is used to identify the plant type using the contour elements of the plant photos. Gradient structured random forest (GS-RF) classification is used to forecast leaf diseases. Principal Component Analysis (PCA) and Hierarchical Gradient Deep Neural Network (HG-DNN) classification techniques are used to determine the causes of a given plant disease based on the characteristics of soil images and plant disease images. The findings are communicated to the growers through text messages sent to their mobile phones on a daily and seasonal basis, along with any potential recommendations for preventative actions.
AI-based vehicular traffic flow forecast for advanced transportation systems
Authors: Panwar Vipin , Khan Gulista , Ganesh D.
Published: September 2023

For traffic control systems, the intricate nature and quantity of vehicular traffic have recently risen significantly. For transportation systems to be optimized and overall efficiency to be increased, accurate prediction of traffic is essential. To anticipate vehicular traffic flow, we introduce a unique reptile-ant optimized bidirectional gated recurrent unit (RAO-BiGRU) method in this study. Ant colony optimization (ACO) and reptile search optimization (RSO) methods are combined in the RAO. The Bi-GRU model's performance is improved by using the RAO to better choose the input features. Extensive experiments are run utilizing the traffic data database to assess the efficacy of the suggested approach. The outcomes show that in terms of predicting accuracy and computational effectiveness, the suggested RAO-BiGRU strategy performs better than the conventional forecasting strategies.
Investigation of arranging doctor schedules during the COVID-19 outbreak by using a decision support system
Authors: Pandey Vijay Kumar , Rastogi Ajay
Published: September 2023

People with minor symptoms are isolated at home, while those with severe symptoms are treated in hospitals. The hospital burden has so increased excessively. Healthcare workers experience physical tiredness as a result of stress. In addition to the growing workload, healthcare providers have psychological issues due to their worry of contracting an infection and spreading it to others. Healthcare personnel needs to be protected and given adequate working circumstances. Since protective gear including gloves that are secure, respiratory protection, and eyewear are crucial for the protection of medical professionals exposed to the virus, medical treatment should also be well arranged for. The fast-spreading COVID-19 epidemic has impacted numerous individuals throughout the globe. Considering the strong infectivity, governments issue advisories to stay at home or enact policies like lockdowns to prevent the spread of the illness.  Arranging doctor scheduling is one of the crucial difficulties. As part of this study, create a doctor scheduling arrangements with a hospital in Turkey during the COVID-19 epidemic. In order to sustain activity in the new departments while lightening the load in the old departments, the hospital built three new COVID-19-related departments. To solve the arranging doctor scheduling issue, provide a Mixed Integer Programming (MIP) paradigm to turn the problem into a Decision Support System (DSS). The consequential schedules maintain healthcare delivery throughout all departments while distributing their effort evenly by reducing the risk of the doctors contracting the illness.
Implementation of novel pattern recognition approach using data mining for medical applications
Authors: Sharma Mohit Kumar , Gupta Namit , Ojha Ananta
Published: September 2023

The capacity of pattern recognition algorithms to analyze complicated data and retrieve useful information has attracted a lot of interest in the world of medical solutions. Data mining methods have become effective tools for pattern detection in the medical field, allowing for the extraction of insightful knowledge from huge and complex datasets. In this study, a diversified convergent squirrel search optimization algorithm and support vector machine (DCSSO-SVM) are combined to provide a unique pattern recognition method for medical applications. The DCSSO algorithm draws its inspiration from squirrels' efficient search-space exploration and exploitation during foraging. The suggested method integrates this algorithm with SVM to enhance feature selection, speed up convergence, and increase classification performance. To assess the effectiveness of the suggested DCSSO-SVM strategy, we assembled a dataset of medical images for this study. Other cutting-edge methods are contrasted with the suggested methodology. The experimental findings show that, regarding the accuracy, precision, f1-score, and recall metrics, the suggested DCSSO-SVM technique performs better than the current methods. It also has great toughness and support, making it appropriate for a variety of medicinal applications.
Analysis of educational data enabled by deep learning to increase student success
Authors: Kumar Gautam , Chaudhary Bulbul , Choudhary Sachin
Published: September 2023

A key component of improving educational quality is identifying pupils who are at a high risk of doing poorly academically as early as feasible. To accomplish this, most studies now in existence have used conventional Deep learning (DL) algorithms to forecast students' academic progress based on their behavior data, from which behavior elements are manually identified owing to the professional expertise and knowledge. Nevertheless, it has become increasingly difficult to recognize finely constructed handcrafted traits as a result of a rise in the types and quantities of behavioral data. The Enriched Plant Growth Optimized Artificial Neural Network (EPGO-ANN) technique enabled data analysis of educational data which is a viable tactic that may be used to improve student accomplishment in educational settings as we suggested in this research. The optimization predicts the academic success by autonomously extracting characteristics from student behavior data from several heterogeneous sources. This model's novelty employs recording the in-built time-series data for each kind of activity and uses EPGO-ANN to extract correlation features between various behaviors. The results of the experiments show that the suggested deep model approach outperforms several DL methods. The results of experiments show that the EPGO-ANN technique is superior to other DL methods.
Predicting future housing prices: a machine learning approach
Authors: Verma Pushpendra Kumar , Arya Satyendra , Asbe Chetana
Published: September 2023

In our ecosystem, the real estate sector is the least transparent. Daily changes in housing prices as well as sometimes exaggerated prices rather than valuations are a part of life in the housing market. Our research project's major focus is on predicting future housing prices using actual machine learning. Here, we want to concentrate our judgments on each fundamental factor that goes into calculating the price. Machine learning has played a significant role in picture identification, spam restructuring, normal speech command, product suggestion, and medical diagnosis in recent years. The current machine learning method aids us in improving security warnings, maintaining public safety, and improving medicinal advancements. Machine learning technology also improves customer service and makes automobiles safer. The current research discusses the prediction of future housing prices provided by a machine learning system. The data was preprocessed after it was collected. In this procedure, we employ the Forest Neural Gradient Boosting Algorithm (FNGBA). We evaluate and compare several prediction techniques for the selection of prediction methods. Our findings demonstrate the necessity for a successful approach to the problem and the capability of our method to provide predictions that can be compared to existing models of housing price prediction. When compared to employing independent methods, the findings showed that this technique delivers the least mistake and the maximum accuracy.
Investigating the Influence of social factors on learning performance in collaborative learning environments
Authors: Kumar Sunil , Shahani Siddharth
Published: September 2023

Developing classrooms for active learning is a more popular component about higher education institutions' instructional efforts to interest and engage students in their academics. Social elements have been determined to impact student learning performance and participation in collaborative learning. This research examined social factors' capacity to encourage collaboration and participation and whether these concepts mediate in connecting social factors and student learning outcomes. The student's academic conduct has been observed using the constructivism theory. Students at universities completed surveys to provide that data. Results assessed using Structural Equation Modelling (SEM) show that societal factors, including connection, social connection, and usage of social media, improve active learning with peers also teachers, collaboration in Education, and participation of students, impacting educational performance. The results support this study's use of two-fold mediation. Online learning should be encouraged because it affects students' academic development in higher education institutions' teaching and learning. Collaborative learning and social component involvement have been found to enhance student learning activities.
Examining the triple bottom line benefits of ecotourism: a multi-faceted approach
Authors: Gupta Sachin , Kumar Sumit , Thakkar Hina
Published: September 2023

This study assessed the economic (Ec), environmental (En), and social aspects (SA) of ecotourism (ET) in the Philippines in Lobo (LO) and Batangas (BT). The Verde Island Passage, where LO is located, is said to be the hub of marine biodiversity on earth. Another characteristic of LO is the 120-year-old Malabrigo Lighthouse, which views Verde Island. Tectona philippinensis trees, which grow in mountain ranges, are severely endangered in the Philippines. There are also fish sanctuaries, lovely beaches, and diving spots there.394 inhabitants of 18 barangays or local villages in the ecotourism regions (ETR), who were chosen by stratified-proportional random selection, provided their opinions. A survey questionnaire was used to acquire data for this descriptive research. Results indicate that ET in L is socially, commercially, and ecologically acceptable. To ensure the viability of ET over the long term, the local Lobo government (LLG) and the Tourist Sector(TS) should exercise continual caution in all aspects of development. Residents of the surrounding areas should also be constantly looking for methods to safeguard the Lobo Environment (LE) and preserve its natural resources, which should take precedence above any financial advantages that industry, tourism, or other sectors may be able to provide.
Assessing the relationship between employment opportunities and criminal tendencies of educated youth
Authors: Deshpande Bhargavi , Goswami Sarita , Shukla Sushim
Published: September 2023

This research examines the hypothesis that criminal activity has increased behavior among knowledgeable young people in Lagos state, Nigeria, in response to the unequal employment opportunities in the public sector. The tool that was used for the purpose of data collection was the questionnaire. Through the use of techniques including simple random sampling and sophisticated sampling, 1900 individuals overall and four geographical regions were selected accordingly. The Statistical Package for the Social Sciences analyzed the acquired data. According to the study, uneven access to work prospects is a key factor contributing to the rise in juvenile delinquency in Lagos. The study's findings indicate that the disparity in hiring practices for open positions in the broader public sector is the cause of the rise in criminal activity among educated Nigerians in Lagos. Consequently, the report recommends that the hiring process in various government entities follows legal procedures and that a supportive business climate that promotes small and medium-sized firms be created.
Increasing car washing competency in adolescents with autism and intellectual disabilities: Researching visual task evaluation
Authors: Panwar Tapish , Gujjar J. Praveen , Arya Satyendra
Published: September 2023

This study looked at how well visual task evaluation helped teenagers with autism and intellectual disabilities become more competent at car washing. For disabled people to promote their independence and employment chances, car washing skills are crucial. The goal of this study was to ascertain whether training techniques that include visual task evaluation can improve car washing proficiency in teenagers with autism and intellectual disabilities. 30 participants, ranging in age from 12 to 18, participated in a pre-test/post-test design. Randomly chosen groups of participants were put into the evaluation group for the visual task or the control group. According to the findings, the visual task evaluation group outperformed the control group in terms of car washing ability. Adolescents with autism and intellectual disabilities can learn skills more quickly and become more independent by including visual task evaluation into their teaching strategies. These results demonstrate the potential for such treatments to enhance their quality of life and employment chances.
Regions with the highest rates of teacher certification and students' cognitive milestones with learning disabilities or emotional/behavioral issues
Authors: Ambulkar Ameya , Rani S. Yavana , Rastogi Nidhi
Published: September 2023

The teacher's special education certification (SPEC) degree is a barometer for their competence in study, practice, and policy. This research compared the test scores of elementary and middle school children with learning disabilities and emotional and behavioural disorders taught by Special Education Teachers (SET) to those of students who General Education Teachers (GET) led in the same subject areas. The type of certification for teachers has little impact on most student groupings. In Learning Disabilities (LD) and regular education, students with LD and more extraordinary intellectual ability performed better in English language arts (ELA) with a trained teacher. Students with Emotional Behavioural Disorders (EBD) who were less proficient in mathematics were less likely to be helped by SET compared to General Education (GE) and Licensed Special Education Teachers (LSET). These words' actual meanings are examined concerning SET quality measures. The outcomes reported here should be understood under several constraints. Operational datasets, like the one utilized in this study, rarely contain data on crucial factors that affect an exceptional education-certified or dual-certified teacher's capacity for effectiveness.
Climate change: role of banks and financial institutions in greening the banking system
Authors: Samueal Samkutty , Singh Rupesh Roshan
Published: September 2023

Global warming and climate change are hot topics of environmentalists globally. The World Bank’s ‘Climate Change Action Plan’ for 2021 to 2025 explains the policies and programs to be implemented by banks and financial institutions to combat climate change. The UN and World Bank have taken actions towards achieving minimal carbon emissions that in turn reduce global warming. In India, national-level financial institutions such as RBI, NABARD, SIDBI and Exim Bank took the lead in formulating policies to achieve the goal of greening the banking system. SBI granted financial assistance for windmills and kick-started green financing activity, followed by all other banks. Financial assistance at the concessional interest rate to renewable energy and energy-efficient projects and buildings, electric vehicles, start-ups, trading green bonds and issues of green mutual funds are introduced by various banks. Additionally, within the bank, a sea change in the form green ATMs, digitalization of banking, issue of electronic bank statements and e-mail communication are started. Various other changes, such as green bank building, purchasing electric equipment conforming to green standards, changing LED lighting systems, conducting conferences and meetings electronically, etc., are implemented in the banks. Thus, both the internal changes within the bank and external changes address the issue of climate change. The study reveals that even though banks have taken the green initiatives a decade back, it is not widely popularized among customers and common people for a nationwide speedy adoption of green banking.
Next-generation attendance tracking: Automated face detection for lecture attendance
Authors: Gupta Sachin , Jain Vipin , Bhat Vinayak Anil
Published: September 2023

Face detection technology has garnered much interest due to its potential uses in industries like face identification and video surveillance systems. In addition to being a component of the automatic face recognition framework, real-time face detection is now becoming its study area. There are numerous methods to handle the problem of face detection. Traditional methods of manual attendance tracking in lecture halls are time-consuming, prone to errors, and lack efficiency. Next-generation attendance tracking systems are incorporating automated face detection technology to address these challenges. This paper explores the implementation of automated face detection for lecture attendance, leveraging computer vision and facial recognition algorithms. Initially, students’ data were gathered and preprocessed using a Median filter (MF). Then the preprocessed data features were extracted using a histogram of gradients (HOG). And finally, extracted features were classified using a Backpropagation Bayesian neural network (BBNN). To prove the efficiency of the suggested method, that is compared and contrasted with specific conventional methods. Experimental outcomes show that the recommended method performs superior when contrasted to traditional methods.
Predicting software defects with swarm-intelligence-based machine learning algorithm for improved process quality
Authors: Gupta Rajesh , Rajkumar Avinash , N. Beemkumar
Published: September 2023

The quality and effectiveness of software systems may be significantly impacted by Software Defects (SD). Therefore, enhancing process quality is essential for controlling and minimizing the incidence of faults. Implementing reliable Software Development (SDe) processes and best practices is one way to do this information. SD, commonly referred to as software bugs or software mistakes, are defects or errors that occur in computer programs and cause them to act up or create unintended outcomes. These vulnerabilities may appear for several causes, including programming mistakes, poor design choices, or issues with the SDe cycle. The prediction of software problems based on Machine Learning (ML) using an Enhanced Artificial Neural Network (E-ANN) is implemented in this study to increase software quality and testing effectiveness. Particle swarm optimization and grey wolf optimization, these two algorithms named grey wolf swarm optimization algorithm (GWSA), are combined to recognize the corresponding compensation of the methods following the respective benefits and drawbacks.  The hybrid algorithm-based model to the conventional hyperparameter optimization strategy and a single swarm intelligence algorithm's investigation of investigational consequences from six data sets shows that the hybrid algorithm-based model has high and enhanced indicators. Processed by the autoencoder, the model's performance has also improved.
A hybrid approach to predicting daily stock market returns with deep learning
Authors: Vinoth S. , Kumar Satish , Jain Manjula
Published: September 2023

It is quite difficult to correctly forecast returns on stock markets because of the economic stock industry' extreme volatility and complex nature. Programming methods of prediction have shown to be increasingly effective at forecasting stock values with the development of artificial intelligence and improved computational capability. Deep learning (DL) algorithms and big data analytics are becoming more and more crucial in a variety of application areas, including stock market investing. However, other research has focused on predicting daily stock market returns, particularly when employing DL approaches to carry out powerful analysis. The DL algorithm is used in this paper's big data analytics approach to forecast the SPDR S&P 500 ETF's daily stock market return direction. The complete dataset was then run through a DL algorithm, such as the MultiDepth NeuroNetwork (MD-NN) technique, to forecast path of the projected index for the stock market daily returns. The simulation results demonstrate that the MD-NN datasets provide much greater classification accuracy than those utilizing the existing approaches.
The African origin of public policy, programme and project monitoring and evaluation (M&E) practices
Authors: Uwizeyimana Dominique E.
Published: September 2023

The literature has robust contestation about the origin of public policy, programme and project monitoring and evaluation (M&E). Cloete (2016) argues that public policy, programme and project M&E originated in Europe and America and was introduced in Africa by colonisers who divided the African continent among themselves in the early 1880s. Cloete (2016) does not explain why there was no public policy, programme or project M&E in Africa or any other non-western part of the world before the arrival of western colonisers. Mouton (2010) also argues that M&E was introduced in Africa by international development organisations and financial organisations in the 1980s. According to Mouton (2010), these western development organisations and financial institutions introduced M&E to the African continent as a condition for accessing foreign aid and as a funding requirement for the projects these institutions were funding. Neither Mouton (2010) nor Cloete (2016) accounts for ample literature which demonstrates that M&E practices existed in Africa (and in China) thousands of years before the arrival of the international development organisation and international financial organisations in these parts of the world. This paper uses the Secondary Data Analysis (Archival Study) approach to provide a systematic and chronological analysis of the available literature to trace the genesis of public policy, programme and project M&E as a practice. The findings in this paper indicate that M&E could potentially have become a practice in African countries such as Egypt and Asian countries such as China long before it was a practice in the U.S. and European countries. However, evidence suggests that academics and practitioners in the U.S. and European countries could have compiled the first recorded theoretical academic content about M&E models and approaches currently being used to train people who want to take up a career or profession in M&E.
The dynamic causality model on foreign institutional investments and nifty indices
Authors: Amudha R. , Kanna K. S. , Dhanalakshmi A. , Rangaswamy Easwaramoorthy
Published: September 2023

Post LPG, the Foreign Institutional Investors (FIIs) Investments, has created a massive impression on the Indian Equity market as well as the Indian economy, contributing toward several financial reforms of the Indian Government, RBI and SEBI, to a major extent, dancing to the tune of, the various Indian and other global economic crisis, to name a few, the US subprime crisis, demonetization in India and the Covid ’19 pandemic lockdown. The recent Covid-19 pandemic triggered fears of global recession due to which, the foreign investors started sculling back to pull out from the equity markets in India, a total net outflow of Rs. 1,12,189 crore (Rs.59,377.1 crore syphoned out from equities and Rs.52,811 crore from the debt segment) the highest withdrawal ever, in March 2020 by the FPIs, had a worst impact on the Indian Equity Market to the extent of a enormous drop of 1203 points of BSE Sensex and the broader NSE Nifty Index tanked 344 points in March 2020. This negative economic effect has prompted to analyze the impact of the FIIs Investments not only on the India’s major Index, i.e.,  Nifty but also its selected sectoral indices. The impact of FII investments has been specifically analyzed on both bidirectional and uni-directional relationship applying Granger causality model, considered more appropriate and have chosen the VAR framework for suitable outcome of the results of this study.
Role of multilevel governance in the prevention of air pollution in Delhi, NCR
Authors: Yadav Dharmendra , Saxena Shalini
Published: September 2023

This study examines the role of multilevel governance in the prevention of air pollution in Delhi, NCR. Theresearch utilises both primary and secondary data sources to assess the perception and opinion of respondents on variousissues related to air pollution. Hypothesis testing is employed to analyse the data, and the findings indicate that less than50% of respondents feel that there is any conflict of interest between agencies or governments in the implementation ofschemes or programs to combat air pollution. Additionally, it is found that more than 50% of respondents believe that theformulation of one apex body for policy formulation, rather than multiple bodies, would be more effective in controllingair pollution. However, 50% of respondents believe that there is a lack of political will or coordination among stakeholdersin controlling air pollution, and multilevel governing bodies are seen as a bottleneck. Based on these findings, it isrecommended that a centralised authority be established to deal with air pollution-related issues and that more efforts bemade to increase public awareness about air pollution. The analytical approach used in this research highlights theimportance of hypothesis testing in making inferences about populations based on sample data. Future research couldalso look into how well different multilevel governance strategies work in different situations. It could also look into therole of citizen engagement and participation in making policies and programmes to stop air pollution. Also, research couldlook at the possible effects on the economy and society of using multilevel governance approaches to stop air pollution.
The difference in the skills employment, job training satisfaction and job engagement by training duration among NDTS Apprentices in Southern region of Malaysia
Authors: Doraisamy Hosalya Devi , Rahman Azmanirah Binti Abdul
Published: September 2023

Globalization, technological advancements, and changes in work organization have increased the demand for skilled employment in many countries. In the absence of skills, employers are less likely to be able to find employment and are more likely to face skills-related unemployment, which decreases their competitiveness. In order to address the challenge of promoting skilled employability, the National Dual Training System (NDTS) apprenticeship training programme is an essential component. This enhances the ability of employees to find employment opportunities and earn income with the help of their skills. The development of skilled employability is one of the most effective strategies for ensuring a successful transition to the workforce and the access to career-oriented employment. Furthermore, the NDTS provides employees with the skills, knowledge, competencies, and attitudes necessary to find employment and to survive in an unstable economic environment. The performance of an employee is also related to the level of job satisfaction along with the skills of the employee. It has been demonstrated that satisfied employees perform better, resulting in a higher productivity level in the organization.  Also, employees must be provided with necessary job training to ensure they develop the necessary skills to fulfill the requirements of their respective roles as well as prepare themselves for employment in specialized fields. The existence of training opportunities is likely to lead to employees remaining satisfied with an organization for a longer period of time. The improvement of employee engagement and workplace excellence can also be observed in this regard. As part of this study, the focus will be on identifying how the duration of the NDTS apprenticeship training program affects the employment of skilled employees. This will affect the satisfaction of job training, and the engagement of employees on the job.
An empirical study of television commercials of FMCG among rural consumers of Odisha
Authors: Parida Prasanta Kumar , Rout Prasant , Pati Jyotirmayee
Published: September 2023

The purpose of this desktop study was to examine the efficacy of e-government technologies and the obstacles to their efficient use. The researchers assessed how well e-government affected service delivery, how it might result in cost savings and the advantages and difficulties of adopting and using it. The study also looked at the advantages of using e-government to investigate this. E-efficiency in providing services was demonstrated by greater citizen involvement and interaction with the local government, increased openness and accountability, and cost savings. The study revealed that the use of e-government improves traditional methods of conducting business and providing services while also enabling access to information and services online, reducing the need for rework, increasing transparency and accountability, and encouraging citizen participation. The ineffective use of e-government in local authorities was hampered by a lack of funding, inadequate enabling infrastructure, low ICT literacy levels, and the digital divide, among other factors. It was evidenced in this study, that e-procurement leads to cost savings, thus it affects cost reduction strategies positively. Thus, e-procurement reduces inventory costs.
Hybrid neural network for non-image-based knee osteoarthritis prediction
Authors: Rathore Abhishek , Bhongade Rakesh Ashokrao , Sharma Man Mohan
Published: September 2023

Osteoarthritis (OA) of the knee is a common degenerative joint condition that adversely affects millions of people worldwide. Early detection and forecasting risks of knee OA can help with prompt interventions and individualized treatment plans to slow the disease's progression. 2 million patients' worth of Taiwanese data was sampled (2001–2015). 1,068,464 comprised control subjects, while 132,594 patients had Knee Osteoarthritis. Over the course of a three-year period, we sequentially used diagnoses, medication, age, and sex to build a feature matrix. In this study, we developed a risk prediction model using a hybrid strategy (CNN-FFNN) that combines a convolutional neural network (CNN) and a feed-forward neural network (FFNN). The performance of the hybrid approach is also evaluated using a variety of performance indicators. Age and sex were excluded from the list of significant disease variables, and drugs like antacids, cough relievers, and stimulants demonstrated discriminative efficacy. The proposed methodology may help medical personnel identify people who are most at risk of getting knee OA, allowing for preventative measures and individualized treatment regimens to lessen the impact of this crippling ailment.
Novel machine learning method in skin cancer diagnosis based on patient's metadata
Authors: Ojha Gajanand , Awasthi Aishwary , Gupta Vinita
Published: September 2023

The skin's fundamental function in human body as a whole-body covering is crucial. Only if it is discovered while it is in its early stages can skin cancer be cured. Skin function plays a big part in the body's overall system and will be significantly impacted by even the slightest modification. The goal of this work was to develop an effective Machine Learning (ML) based technique for identification of skin cancer using patient information. To diagnose skin cancer with lesions image, this research introduces a novel Augmented May Fly optimized with K-Nearest Neighbors (AMFO-KNN) technique. Here, the AMFO approach is used to improve the classification efficiency of KNN. Utilizing the PAD-UFES-20 and Fitzpatrick17k datasets, the efficiency of suggested method is examined. The noisy data are removed from the raw data samples using Adaptive Median Filter (AMF). The properties are taken out of segmented data using Kernel Principal Component Analysis (KPCA). The performance metrics of research show that recommended methodology performs better than traditional approaches in terms of accuracy, precision, f1-score, and recall measures. The encouraging results demonstrate the effectiveness of suggested strategy and show that including the patient's information with lesions image may improve the performance of skin cancer diagnosis.
Correlation of pulp stones to hyperlipidemia and hypertension: a clinico pathological hospital-based study
Authors: Asha K. , Verma Shikhar , Yeluri Garima
Published: September 2023

Pulp stones signify the calcification of the discrete origin seen in the pulp tissues and are seen either embedded or attached to the dentin. Pulp stones can be a pathological or physiological entity. The pathogenesis of the pulp stone formation is idiopathic. However, various theories have been proposed. Pulp stone frequency is in the range of 8% to 90%. They are more identified on the histopathologic examination than radiographic assessment. The present study aimed to assess and correlate the histopathologic analysis and clinical parameters of the pulp stones to hyperlipidemia and systemic hypertension. The study assessed 140 subjects for the correlation of histopathologic analysis and clinical parameters of the pulp stones to hyperlipidemia and systemic hypertension. After inclusion, detailed case history was recorded for all the participants. This was followed by the radiographic assessment, and the pulp tissue after extirpation was sent for processing. The data gathered was statistically assessed with the Chi-square test and SPSS software. The study showed that on radiographic assessment, pulp stones were seen in 28 subjects and histopathologic assessment in 60 subjects among the total studied 140 subjects. Hyperlipidemia and hypertension were significantly associated with using nonlaminated and uneven varieties of pulp stone. The study concludes that subjects having irregularly shaped and nonlaminated pulp stones on histopathologic analysis should undergo assessment for systemic hypertension and hyperlipidemia.
Addictive digital media use and psychiatric symptoms: a cross-sectional study
Authors: Mishra Saubhagya , Malathi H. , Deep Prakash
Published: September 2023

The past 10 years have seen a substantial change and increase in the research of "addictive technological behaviors." Additionally, studies have found a strong association between concomitant psychiatric issues and technological dependency. In the present study, 23,533 Participants in a cross-sectional online study of adults survey to investigate potential role of demographic variables, attention deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), in the a range of addictive use, which is defined as compulsive and excessive use linked to undesirable effects, there are two forms online technologies of the present: YouTube and social media. We made use of instruments with robust psychometric capabilities. Every substantial and positive correlation the interaction between the two addictive technological behaviors when contrasting the signs of various mental problems and those of addicted technology use. The compulsive utilization of these technologies and age seems to be unrelated. Being a man was substantially correlated with a video game addiction, but being a woman was strongly correlated with a social media addiction. Addiction to online gaming and social networking was positively correlated with being single. According to hierarchical regression research, demographic traits approximately 11% to 12% of the difference in the use of addictive technologies was explained by this. Mental health-related variables accounted for 7% to 5% of the difference. The study significantly expands our knowledge of the signs of mental illness and how they relate to technological addiction. Discussions of the clinical relevance, limitations, and strengths are included.
Psychological skills in athletic rehabilitation (AR): trainers and therapists perceptions
Authors: Gupta Prerana , Balla Suhas , Verma Sneha
Published: September 2023

Sport-injury recovery is said to benefit from psychological abilities, yet there is little use of mental imaging in treatment protocols. To determine if the opinions of Physical Therapists (PTs) and Athletic Trainers (ATs) on the healing process for harmed sportsmen may be sped up by the use of positive language, setting targets, and mental imagery in treatment. To participate in an experiment that was only administered once and asked participants about the efficiency of psychological techniques, the ATs and PTs were approached for increasing adherence and speeding up the healing of wounded sportsmen enduring treatment. A total number of 309 ATs and 356 PTs responded out of the one thousand ATs and PTs that were randomly selected. The Attitudes about Imagery (AAI) study gauges opinions on psychological techniques for improving injured athletes' commitment and recovery speed. The AAI asks questions on demographics and measures attitudes regarding the influence of pain management on injured athletes' compliance with therapy and utilizes fifteen inquiries and a Likert scale with seven points to measure recovery speed. To ascertain if variations in views occurred due to the professionals' educational background, training experience, and interest, we performed 1-way analyses of variances. The usefulness of psychological skills to support the recovery process was generally viewed favourably by ATs and PTs. The utilization of mental abilities is considered in terms of clinical ramifications.
Подовження інтервалу QT як значуще кардіоваскулярне ускладнення під час системного лікування хворих на рак
Authors: Kozhukhov S. M. , Deyak S. I. , Bazyka O. Ye. , Dovganich N. V. , Yarynkina O. Ya. , Tkhor N. V.
Published: September 2023

The widespread introduction of new anticancer drugs and treatment technologies has contributed to an increase in the survival rate of cancer patients. At the same time, the development of cardiovascular complications in the setting of anticancer treatment often leads to a decrease in the quality of life and worse prognosis.One of the manifestations of cardiovascular complications during cancer treatment is QT prolongation with various cardiac arrhythmias.The article is based on the analysis of randomized clinical trials and meta-analyses, and the recommendations of the European Society of Cardiology on Cardio-Oncology. The review describes the risk factors and classification of QT prolongation caused by anticancer therapy.The main groups of anticancer drugs that induce changes in the QT interval are analyzed. The methods and frequency of examination of cancer patients with various pathological conditions and comorbidities that can lead to QT prolongation during and after cancer treatment are considered.The review is devoted to the analysis of changes in the QT interval and clinical application of decision-making algorithms to minimize cardiotoxicity in order to improve the effectiveness of anticancer treatment.