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Transmit Range Adjustment Using Artificial Intelligence for Enhancement of Location Privacy and Data Security in Service Location Protocol of VANET
IoT or the internet of things is the talk of the town topic being researched in the field of information technology for more than decade. It is being in deployment stage in various developing economics, to enable driverless automobiles in the field of VANET. It helps in preventing crashes and provides urgent medical assistance in emergency case. Data security and location privacy are becoming of most importance in present IT scenario. Unauthorized access to location information of vehicles may pose a significant security threat. So, it is necessary to secure the location information of the vehicle. The proposed work aims at enhancement of location privacy data security in service location protocol of VANET'S. The primary techniques to be employed include artificial intelligence-based RF range approximation for transmission range adjustment and receive RF strength based distance estimation for trusted node location perimeters approximation, dynamic adjustment of silence period of OBU (on based unit) in conjunction with radio/RF interrupt. The unauthorized access to location information of vehicles and need of its privacy is the motivation for this work. 2022 Shivkant Kaushik et al. -
Cryptographic key distribution using artificial intelligence for data security and location privacy in VANET
Location privacy & data security in VANET are now becoming most important in todays paradigm of information age. Unauthorized access to location information of vehicles may pose a significant security threat, thus it is necessary to secure this information from intruders. In proposed work, Artificial intelligence based RF range approximation is used with multi key controlled cryptography for enhancement of location privacy and data security in service location protocol of VANETS. 2022 Taru Publications. -
Artificial intelligence: A new model for online proctoring in education
As a result of technological advancements, society is becoming increasingly computerized. Massive open online courses and other forms of remote instruction continue to grow in popularity and reach. COVID-19's global impact has boosted the demand for similar courses by a factor of ten. The ability to successfully assign distant online examinations is a crucial limiting factor in this next stage of education's adaptability. Human proctoring is now the most frequent method of evaluation, which involves either forcing test takers to visit an examination centre or watching them visually and audibly throughout tests via a webcam. However, such approaches are time-consuming and expensive. In this paper, we provide a multimedia solution for semi-automated proctoring that does not require any extra gear other than the student's computer's webcam and microphone. The system continuously monitors and analyses the user based on gaze detection, lip movement, the number of individuals in the room, and mobile phone detection, and captures audio in real time through the microphone and transforms it to text for assessment using speech recognition. Access the words gathered by speech recognition and match them for keywords with the questions being asked for higher accuracy using Natural Language Processing. If any inconsistencies are discovered, they are reported to the proctor, who can investigate and take appropriate action. Extensive experimental findings illustrate the correctness, resilience, and efficiency of our online exam proctoring system, as well as how it allows a single proctor to simultaneously monitor several test takers. 2023 Author(s). -
Multi-class SVM based network intrusion detection with attribute selection using infinite feature selection technique
An intrusion detection mechanism is a software program or a device that monitors the network and provides information about any suspicious activity. This paper proposes a multi-class support vector machine (SVM) based network intrusion detection using an infinite feature selection technique for identifying suspicious activity. Single and multiple classifiers generally have high complexity. To overcome all the limitations of single and multiple classifiers, we used a multi-class classifier using an infinite feature selection technique, which performed well with multiple classes and gave better results than other classifiers in terms of accuracy, precision, recall, and f_score. Infinite feature selection is a graph-based filtering approach that analyses subsets of features as routes in a graph. We used a standard dataset, namely the UNSW_NB15 data set generated by the IXIA perfect-storm tool in the Australian Centre for Cyber Security. This dataset has a total of nine types of attacks and 49 features. The comparative analysis of the manuscript work is done against eight different techniques, namely, hybrid intrusion detection system (HIDS), C5, one-class support vector machine, and others. The proposed work gave better simulation results using the 2015a Matlab simulator. 2021 Taru Publications. -
An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid
The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience. 2024 IEEE. -
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HAPPINESS INDEX OF HIGHER EDUCATION STUDENTS TOWARDS ONLINE LEARNING IN INDIA
World Happiness Index generally indicates the level of happiness and satisfaction among the residents in a given country. Since we all know that worldwide new ecosystem of online education has evolved there are many countries which have done pretty well with respect to adopting of technology in the education others have been lacking behind and hence causing more inequality in the online education space. To understand the students' perception and satisfaction regarding the online learning this study was conducted to assess the relationships of the happiness index (HI) and related parameters which were retrieved from existing literatures and self-prepared parameters. Accordingly, the world happiness index signifies a direct relationship with the social economic development factors leading to the general well-being of individuals and societies that include the full development of healthcare, politics, and higher employment. The question arises has the online learning lived up to its potential? The Indian Education System is heterogeneous comprising of private and public universities. The study was on conducted in the National Capital Region of India, (NCR). The data was collected from various types of universities' students irrespective of the gender, caste, creed and religion. The study aims to understand the perception of the students and challenges faced by them during the online learning. It is very important to know the views of the students along with teachers to get the true ground reality of online learning in India. Since the pandemic have hit overall the world education sector was hit too. All the educational institutions were closed for about nearly 1.5 years. There was drastic shift in the paradigm from traditional learning to the online learning. To understand the students' perception data is being collected from around 268 students of the Delhi NCR region. The study is quantitative. The questionnaire was distributed to the both Government and Private Universities to understand students' satisfaction regarding online learning. The data was being analyzed in the graph form. The study says the future of online learning is possible provided students have access to devices and better connectivity. 2022 Zeitschrift fur Psychologie / Journal of Psychology.All rights reserved. -
IoT in schools: Revolutionizing education through smart technology
The Internet of Things (IoT) is rapidly transforming various sectors, and education is no exception. This chapter explores the potential and applications of IoT technology in the education sector, shedding light on how it can revolutionize teaching, learning, and overall school management. By seamlessly integrating smart devices and applications into the classroom environment, IoT creates an interconnected and efficient learning ecosystem. The discussion covers the current state of IoT adoption in education, highlighting the benefits, challenges, and future prospects of this technological integration. 2025 selection and editorial matter, Adesh Kumar, Surajit Mondal, Gaurav Verma, and Prashant Mani; individual chapters, the contributors. -
A study of Autoregressive Model Using Time Series Analysis through Python
A Time-series investigation is a simple technique for dividing information from reconsideration perceptions on a solitary unit or individual at ordinary stretches over countless perceptions. Timeseries examination can be considered to be the model of longitudinal plans. The most widely used method is focused on a class of Auto-Regressive Moving Average (ARMA) models. ARMA models could examine various examination questions, including fundamental cycle analysis, intercession analysis, and long-term therapy impact analysis. The model ID process, the meanings of essential concepts, and the factual assessment of boundaries are all depicted as specialized components of ARMA models. To explain the models, Multiunit time-series plans, multivariate time-series analysis, the consideration of variables, and the study of examples of intra-individual contrasts across time are all ongoing improvements to ARMA demonstrating techniques. [1] 2022 IEEE. -
The Preservative Technology in the Inventory Model for the Deteriorating Items with Weibull Deterioration Rate
An EOQ model for perishable items is presented in this study. The deterioration rate is controlled by preservative technology. This technology only enhances the life of perishable items. So, retailers invested in this technology to get extra revenue. The Weibull deterioration rate is considered for the ramp type demand. Shortages consider partially backlogged, and discount is provided to loyal customers. The concavity of the profit function is discussed analytically. Numerical examples support the solution procedure; then, Sensitivity analysis is applied to accomplish the most sensitive variable. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Punishing poverty: The economic disparity of the poor in the criminal justice system
Equality before law is one of the most significant features of the Indian constitution. Anyone who seeks justice must be provided with legal support without any discrimination. An accused is also assured of penalization based on the tenets of equality irrespective of his ethnicity, religion, economic, social background, etc. Poor parity has led to discriminatory approaches in awarding punishments to offenders belonging to economically marginalised sections of society. The low paying capacity of the poor offenders gives an upper edge to the rich offenders who has better paying capacity of fines or damages and suffer less severe repercussions through the justice system. This paper will conduct a comprehensive study to identify the discrepancies in the penalization process and its implications in the dispensation of justice. It will also explore the factors such as social background, ethnicity, and economic status which play an integral part in influencing the legal and sociological perspectives of the stakeholders of the justice delivery system. It will analyze the judicial trend and legislative framework to ensure equitable justice. It will conclude with suggestions and recommendations for the formulation of robust policies to ensure a just penal administration. 2026 The Author(s). -
Coati Optimization Algorithm for Detecting Pediatric Kidney Abnormalities using Ultrasound Images
This study aimed to classify pediatric ultrasound images as normal or abnormal by identifying the optimal number of image texture features for analysis and developing an effective classification system using selected features. The experiment identified a successful feature selection and classification algorithm with a good performance. This study introduced a new approach for computer-assisted ultrasound image classification. Initially, a Gaussian median filter enhances the image quality and removes noise. For feature extraction, various features, including first-order derivatives, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Dependence Matrix (GLDM), Gray Level Size Matrix (GLSZM), and Neighbouring gray tone difference matrix (NGTDM), were extracted using the Pyrandiomics Python package. The Coati optimization algorithm (COA) was employed as a feature selection technique. The Classification was performed using Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-nearest Neighbor (KNN), Nae Bayes (NB), and Extreme Gradient Boosting (XG-Boost) algorithms. Therefore, this study proposed a new machine learning classifier, the Extreme Gradient Neighborhood classifier (XGNC), using NB, KNN, and XG-Boost, with a classification accuracy of 97.91%, which outperformed the other classifiers mentioned in the study. The results indicated that the optimal feature selection and classifier choice yielded the most accurate computer-aided diagnosis of kidney abnormalities. 2025, Iquz Galaxy Publisher. All rights reserved. -
Optimized Feature Selection for Kidney Ultrasound Image Classification Using Binary Coati Weighted Mean Vector Algorithm
The analysis of medical images presents many challenges, especially when making precise diagnoses. In pediatric Chronic Kidney Disease (CKD), early identification is critical because of its gradual progression to significant kidney failure. This study proposes a diagnostic framework for pediatric ultrasound image classification that incorporated machine learning and advanced feature selection methods. This approach is divided into four stages: Preprocessing, feature extraction, feature selection, and classification. Initially, pediatric kidney ultrasound images are enhanced using gaussian median filter. Radiomics features were then extracted, including Gray Level Co-Occurrence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), and first-order statistics. To optimize this feature space, we introduce the Binary Coati Weighted Mean Vector (BinCoWmv) optimization algorithm, which uses a customized fitness function. Herein, the selected features were evaluated using different classifiers: Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Nae Bayes (NB), K-Nearest Neighbor (KNN), and XG-Boost. Comparative evaluations with existing optimizers, such as the Coati Optimization Algorithm (COA), weighted average vector (INFO), Firefly Algorithm (FFA), and Harris Hawk Optimization (HHO), showed that BinCoWmv achieved a higher classification accuracy. Our framework improves diagnostic reliability and assists radiologist and nephrologist in the early detection of chronic kidney disease in children. 2025 Fizhan Kausar and Ramamurthy B. -
Recent trends in photocatalytic water splitting using titania based ternary photocatalysts-A review
Hydrogen is considered as an ideal fuel, and its use has several advantages. While several methods are available for producing hydrogen, photocatalytic water splitting using semiconductor-based photocatalysts is one of the better methods. Among the various semiconductors, titania, having many desirable properties, is a widely explored photocatalyst material to fabricate ternary heterojunctions. Preventing the recombination of photoexcited charge carriers, reducing the band gap, and enhancing the migration of charges are steps needed to improve the efficiency of the photocatalysts. Various modifications have been made to the structural and chemical properties of the photocatalysts. While innovative synthetic protocols can bring about the desired changes, incorporating metal oxides and noble metals with varied morphologies into titania leads to multijunction photocatalysts. Structural modifications to titania include incorporation of various nanostructured materials, noble metal nanoparticles, transition metal chalcogenides, polymer materials, semiconductors like g-C3N4, quantum dots, etc. 2022 Hydrogen Energy Publications LLC -
Transforming Pediatric Healthcare with CKD using AI: A Systematic Mapping
Artificial intelligence has been used on a much larger scale, from self-driving cars to biometrics. The daily lifestyle of civilization has changed dramatically due to scientific growth. AI has been pushed to a wide range of applications rather than limited to certain areas and has benefited the health industry, resulting in improved outcomes. Heuristics, support vector machines, artificial neural networks, and natural language processing are some of the AI approaches employed. Kidney diseases and treatment can be challenging, especially when working with youngsters. Children with Chronic Kidney Disease (CKD) experience a wide range of symptoms classified as either transitory or nosologic. Some of its traits influence not only during childhood but also during adulthood in the long run. This study will focus on strategies utilized to identify, predict, and categorize the impacts of pediatric kidney disorders in terms of aetiology, clinical features, and medicines that might assist children in transition to adulthood smoothly. 2023 IEEE. -
Optimizing Kidney Ultrasound images through Pre-Processing Filters
Medical image processing and analysis have greatly advanced in the past decade, significantly contributing to the diagnosis of various diseases.However, It is crucial to address the need for effective data management in the medical field due to the significant rise in data generation and storage. It necessitates the exploration of compression methods as a means of achieving efficient data handling. Consideration should be given to image processing approaches to minimize redundancy. Ultrasound imaging has gained importance in recent years, but the presence of artifacts in ultrasound images has complicated diagnoses. An evaluation has been performed to identify appropriate Pre-processing techniques for kidney images before extracting kidney features. Observing the sensitivity and calculating the PSNR and MSE of the filtered image are used to assess the applied methods. The results indicate that the median filter is ideal for image quality enhancement, while the Sobel filter is highly effective in detecting kidney edges. 2023 IEEE. -
Machine Learning Based Optimal Feature Selection for Pediatric Ultrasound Kidney Images Using Binary Coati Optimization
Chronic kidney disease (CKD) one of the most dangerous illnesses. Early detection is vital for improving survival rates and underscoring the need for an intelligent classifier to differentiate between normal and abnormal kidney ultrasound images. Features extracted from an image have a significant impact on classification accuracy. In this study, we present a Binary Coati optimization algorithm (BCOA) for feature selection in CKD, which focuses on reducing the high dimensionality features extracted from ultrasound images, including GLCM, GLRLM, GLSZM, GLDM, NGTDM, and first order, by employing BCOA-S shaped and BCOA-V shaped transfer functions that convert BCOA from a continuous search space to a binary form, which helps in the selection of optimal features to improve the classification performance while reducing the feature dimensionality. The reduced feature was evaluated using six machine-learning classifiers: Random Forest, Support Vector Machine, Decision tree, K-nearest Neighbor, XG-boost, and Nae Bayes. The efficiency of the proposed framework was assessed based on accuracy, precision, recall, specificity, f1 score and AUC curve. BCOA-V outperformed in terms of accuracy, precision, recall, specificity, F1 score and AUC curve by 99%,100%,97%,100%, 98%, and 98%, respectively. This makes it a superior choice for CKD diagnosis and is a valuable tool for feature selection in medical diagnosis. (2024), (Intelligent Network and Systems Society). All rights reserved. -
Assessing global perceptions of India: Policy implications drawn from foreign tourism narratives
This study scrutinizes Indias growing appeal as a tourist destination, accentuated by government initiatives and innovative tourism policies like the e-visa program, Incredible India Campaign 2.0 and digital advancements in the travel sector. With the diminishing impact of COVID-19, there is a noticeable surge in various forms of tourism inbound, outbound and domestic. The primary focus is to understand the driving factors behind the choice of India as a destination for inbound tourists. This research delves into these motivations, providing a global perspective on Indias attractiveness. A mixed-method approach was employed, utilizing convenience sampling for data collection. The quantitative analysis was based on a survey, informed by a literature review, comprising 390 respondents from 10 diverse Indian destinations. Additionally, 25 qualitative interviews were conducted, aiming to enrich and triangulate the quantitative findings. Exploratory factor analysis (EFA) revealed five predominant motivations among inbound tourists: culinary interests, spiritual pursuits, budget-consciousness, cultural curiosity and natural allure. These findings were substantiated through thematic analysis. The outcomes have significant practical ramifications for destination managers and tourism policy developers in India. By understanding these key motivators, they can devise targeted strategies for enhancing the appeal of India to these specific tourist segments. This study not only aids in refining tourism promotion efforts but also contributes to the academic discourse on tourist motivation offering a fresh international perspective on Indias image as a tourist destination. by the author, licensee University of Lodz Lodz University Press, Lodz, Poland. -
Impact of corporate governance on financial performance of information technology companies
Corporate Governance is a broad term in todays competitive world. It is a series of processes, policies, rules, and regulations by which companies are managed and governed. In this perspective, the study attempts to analyze the impact of corporate governance on the financial performance of Information Technology (IT) Companies in India. Specifically, the study analyzed the impact of Board size, Board Composition, and Audit Committee Independence on Return on Assets and Return on Equity, which are considered as measures of financial performance. The findings of the study revealed that there is a significant and positive impact of Corporate Governance on Financial performance of IT companies, and Audit Committee Independence shows the most significant effect on Financial performance. The finding of the study endeavors to contribute to the limited literature available in the context of corporate governance in IT companies in India. BEIESP. -
Exploring the Blockchain-Enabled Metaverse: A Comparative Study of Leading Platforms
The integration of metaverse and secure-based blockchain is transforming several domains, including the area of virtual employment fairs. This chapter comprehensively examined technologies and covers the areas and platform that is both immersive and secure for job searchers and recruiters. It provides a novel case study of a virtual job fair, focusing on its system architecture with metaverse and blockchain. The Decentraland platform is focused and comprises essential elements for metaverse environment and blockchain network. This will help through analyzing as well as interactions between attendees, recruiters, and system administrators the operational process, with an improved security, transparency, and user engagement. The study recognizes promising advancements, yet it accentuates important obstacles and unsolved issues, such as expansion, data protection, and portability. These concerns must be addressed in order to fully exploit the promise of the metaverse and blockchain in revolutionizing virtual interactions. 2025 Scrivener Publishing LLC.