bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Covid-19 Pandemic on User Search Behavior: A Case Study of Postgraduate Students
Authors: Obada Alhabashneh Et al.
Published: January 2024

Relevance Feedback (RF) is crucial for building a user profile which is a fundamental element of different intelligent systems such as information retrieval, information filtering, and personalization. RF is affected by a number of contextual factors such as mood, stress level, and sentimental state of the user. Covid-19 pandemic imposed dramatic changes to the user environment as well as the search context. This paper investigates user’s search behaviour to identify the differences in the behavior between the contexts before and during the Covid-19 pandemic. This can be practically translated into identifying the differences in the relationship between the implicit feedback and the explicit relevance level between the two contexts. For this purpose, we conducted three user studies (i) Pre-COVID-19,  (ii) Mid COVID-19 and (iii) after Covid-19. A user study was conducted on the same group of users on the three user studies. The Pre-COVID-19 user study took place before the pandemic started and the Mid-COVID-19 user study took place three months after the beginning of the pandemic. After Covid-19 stage took place after 18 months of the pandemic. A linear regression model was developed for each user study using IBM-SPSS. The analysis showed a significant variation in the user behavior between the two studies due to the COVID-19 context and its impact on user search behaviour. Also, two new RF parameters in Mid-COVID-19 were shown to have a significant relationship with the explicit user interest which were Mouse Clicks and Page/Down strikes. Furthermore, the comparison between the two models showed that the second regression model achieved a higher accuracy level that is attributed to the common behavioral change imposed by the pandemic.
Design of a Novel Convolutional Deep Network Model for Car Accident Prediction
Authors: V Raviteja Kanakala Et al.
Published: January 2024

Real-time collision risk estimation is thought to be essential to a sophisticated traffic management system. To swiftly determine accident probability is the goal of real-time crash risk prediction.  However, due to the complex traffic situation on urban arterials, urban arterials were rarely included in previous studies, which mostly focused on highways. This paper suggests using Convolutional Deep Network model (CDNM) to forecast the probability of vascular accidents in real time.  This model has the benefit of being able to use both LSTM and CNN.  CNN retrieves the time-invariant characteristics, while LSTM captures the data's long-term dependability. To estimate the likelihood of an accident, many sorts of data are used, including weather, traffic, and signal timing data. There are also many other data preparation methods employed. The problem of data imbalance is also addressed by normalization which oversamples the crash cases. Using a variety of measures, the CDNM is enhanced on the training data and assessed on the test data.  Five more benchmark models are constructed for model comparison. K-NN, ISVM, ANN, CNN, CNN-EVT and GAN are some of the models in this group. Experimental findings show that the proposed CDNM beats the competition in terms of sensitivity, specificity, accuracy, AUC and G-mean value. The findings of this paper demonstrate that CDNM can real-time prediction of crash risk at arterials.
Navigating Ethics in Digital Humanities: A Deep Dive into Decision Distribution within Higher Education at the University of Jordan
Authors: Imad Salah Et al.
Published: January 2024

This research aims to aid higher education institutions in making decisions that align with student needs and enhance their satisfaction. It considers decision presentation, timing of implementation, and communication of the benefits tied to educational quality improvements. To gauge student opinions, an online questionnaire research design was adopted, involving 3,000 male and female students from the University of Jordan. Findings indicated that students generally express dissatisfaction with higher education decisions and regulations due to unclear communication and limited implementation time.

For predicting educational quality outcomes, four machine learning algorithms were employed, each corresponding to four different higher education decisions. Notably, the Random Forest (RF) algorithm showcased superior performance. In the initial questionnaire, it achieved an accuracy of 97%, which slightly decreased to 92% in the second questionnaire due to the expanded dataset and varying factors affecting accuracy. The k-Nearest Neighbors (KNN) algorithm also yielded impressive results, achieving a remarkable 94% accuracy in the third questionnaire.

In the third questionnaire, the Decision Tree (DT) algorithm exhibited an accuracy of 85% in optimal scenarios. In contrast, the Convolutional Neural Network (CNN) algorithm, tailored for intricate tasks with numerous variables, consistently performed below expectations across all questionnaires. Its efficacy consistently lagged alternative algorithms, indicating a misalignment with the specific demands of its operational framework.
In Silico Screening of Ellagic Acid Derivatives and Their Metabolites as Inhibitors for NF-?b and Tnf?
Authors: Sullim Lee Et al.
Published: January 2024

The aim of this study was to evaluate inhibitory effects of ellagic acid derivatives and their metabolites on tumor necrosis factor ? (TNF?) and nuclear factor-?B (NF-?B). In silico molecular docking was performed. 3-O-methyl-3', 4' -methylenedioxy ellagic acid (MMDEA) and urolithin D were to be potent inhibitors of TNF? among tested molecules. For NF-?B (p50 and p65 subunits), MMDEA was found to be a potent inhibitor comparable with tanshinone IIA, a known NF-?B inhibitor. Ellagic acid derivatives seemed to be more effective than urolithins, their metabolites. All of the ellagic acid derivatives and their metabolites were predicted to have good pharmacokinetical properties according to Lipinski's rule of five. These results suggest that ellagic acid derivatives may potentially down-regulate inflammatory responses..
A Study on Financial Performance of Selected Public Sector Banks
Authors: Balambigai Subramanian Et al.
Published: January 2024

Public sector banks are those in which the government holds more than 50% of the total stock. The government formulates all the financial guidelines for public sector banks. The public sector banks operate under the government to inspire trust in the depositors that their money is safe. The aim of the study is to find out the financial performance of selected Public Sector Banks using ratio analysis. Tools used for the study Mean, F-test or ANOVA (Analysis of Variances). Conclusion of the study Public sector banks want to increase the management capability to increase the profits,ROE, EPS, ROI and to increase the efficiency of banks.
Hypervisor-Level Ransomware Detection in Cloud Using Machine Learning
Authors: Prasad Purnaye Et al.
Published: January 2024

Ransomware attack incidences have been on the rise for a few years. The attacks have evolved over the years. The severity of these attacks has only increased in the cloud era. This article discusses the evolution of ransomware attacks targeting cloud storage and explores existing ransomware detection solutions. It also presents a methodology for generating a dataset for detecting ransomware in the cloud and discusses the results, including feature selection and normalization. The article proposes a system for detecting attacks in virtualized environments using machine learning models and evaluates the performance of different classification models. The proposed system is shown to have high accuracy of 96.6% in detecting ransomware attacks in virtualized environments at the hypervisor level.
Machine Learning Algorithms in Cloud Manufacturing - A Review
Authors: Syed Omer Farooq Ahmed Et al.
Published: January 2024

Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment.
Transforming Education: Understanding How Social Innovation Makes a Difference in Education Sector
Authors: Aarti Mohan,Arpita Et al.
Published: January 2024

In the face of persistent educational challenges and the evolving demands of the 21st century, social innovation has emerged as a beacon of hope, offering a transformative approach to reimagine and reshape education systems. This paper delves into the concept of social innovation in the education sector, elucidating its defining characteristics, examining its profound benefits, and acknowledging the challenges it presents. Through a comprehensive review of existing literature, the paper showcases compelling case studies and examples of successful social innovations that have revolutionized educational landscapes. Education, the cornerstone of societal progress, faces persistent challenges in the 21st century. Social innovation emerges as a beacon of hope, offering a transformative approach to reshape education systems. Social innovation in education aims to address social needs, fosters collaborative partnerships, and prioritizes sustainability. It seeks to dismantle educational inequities and empower all learners to reach their full potential. The benefits of social innovation include enhanced learning outcomes, championed equity and inclusion, and preparation for the future. However, sustainability, scalability, measurement, and collaboration pose significant challenges. A growing body of research showcases successful social innovations that have revolutionized educational landscapes. Embracing social innovation can pave the way for a more equitable, effective, and future-ready education system.
Detecting Power System Voltage Stability in Large Test System
Authors: Ch. Hemanth Kumar Et al.
Published: January 2024

The unpredictable changes in electricity demand, along with potential risks, require power system operators to act quickly to prevent voltage instability. To do this, real-time monitoring is crucial, especially in larger systems, but it is a challenging task for power system engineers. The existing methods for detecting voltage instability need to be tested and validated for complex systems. This study aims to validate a voltage instability detection index called SQLVIDI in a larger test system with 118 buses (IEEE 118 bus test system). The simulation results manifest that this index has the capability to detect potential voltage instability in the power system with larger number of buses.
Detection of Diabetic Retinopathy Through Identification of Blood Vessel Thickness Using-FOFF
Authors: G.Indira Devi Et al.
Published: January 2024

Diabetes is one of the main problems in today's medical industry. Identification and diagnosis of the eye condition brought on by diabetes' elevated blood sugar levels are crucial. The motive of the work is to present a useful technique for system diagnosis utilizing a retinal fundus picture. A four-stage implementation paradigm is created in this study. Initially the input image is pre-processed, secondly blood vessel segmentation is performed; thirdly feature extraction and finally classification. In this paper an effective methodology is utilized by introducing meta heuristic algorithm. In the entire process, blood vessel segmentation (BVS)contributes a crucial part in DR detection for which a firefly optimized frangi filter (FOFF) is designed in this paper. The process of categorization comes last. The classifiers K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are employed. The performance of system is evaluated by computing the accuracy and precision. The results are compared between the two utilized classifiers. The SVM performance is good with an accuracy of 95.5% and KNN having an accuracy of 91.6%.
Model-Based Testing Approaches using UML Diagrams: A Systematic Literature Review
Authors: Jyoti Gautam Tiwari Et al.
Published: January 2024

Software Unit Testing (SUT) is the starting point for Model-Based Testing (MBT), a testing method. The Unified Modeling Language (UML) has become the standard for modelling software in professional and academic settings. There are various uses for the modelling language known as UML. The findings of an SLR on UML-based model-based testing methodologies are presented in this paper. Thirty-five primary articles about six research issues were examined using selection and exclusion criteria. Methods, model class, intermediate format use, and testing methodology are the primary points of examination. The review outcomes identify future research needs and avenues of inquiry.
Freshness Detection and Classification of Chicken Eggs using Spectroscopy
Authors: Priti Prakash Patil Et al.
Published: January 2024

The poultry industry plays a pivotal role in India's economy, with particular emphasis on egg production. India ranks as the world's third-largest producer of chicken eggs. Eggs are a delicate component of the human diet, and their quality can undergo substantial changes during storage. This has implications for egg quality and the classification of chicken eggs, both of which are critical factors affecting the poultry sector. Globally, numerous chicken breeds are being developed, necessitating the classification of eggs based on breed due to varying atmospheric conditions required for their storage. However, in India, there is a lack of technical methods for classifying eggs from different chicken breeds. The primary challenges faced by the poultry industry in India revolve around maintaining egg freshness and accurately classifying eggs by breed. While developed countries employ grading systems for eggs, this practice is less common in developing nations like India. To address these challenges, this study aims to propose a model that utilizes spectroscopy as a non-destructive method for assessing egg quality and freshness. The model seeks to establish a link between spectral data, collected using a handheld SCiO NIR spectrometer with wavelengths ranging from 740nm to 1070nm at a spectral resolution of 1 nm, and established destructive methods, particularly Haugh Units, to determine egg freshness based on storage duration.
Speech and Text based Assistive Application for Special Needs Individuals
Authors: Ashwini Rao Et al.
Published: January 2024

As per the survey report released last year on disability by National Statistics Office, it was seen that people with some kind of mental or physical disability is around 2.2% of India’s total population. The project, “Saksham” that suggests be independent, aims to eliminate the need for human assistance and to provide equal opportunities and a more normal way of life to those with language or other disabilities. In the direction of building individual strength and also get great improvement in the academic performance of adults and kids with special needs, Assistive technology is now being used as an innovative tool. The entire application have been developed keeping in mind that it needs to provide all our users with instantaneous access to selected features specially catered to help them in completing their daily tasks.
An Empirical Study of Destructive Nodes Characterization in Wireless Networks
Authors: Srinivas Aluvala Et al.
Published: January 2024

Mobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms.  Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs.
Collaborative Applications of Internet of Things in various spheres of life: Past, Present and Future
Authors: Tanuja Patwal Et al.
Published: January 2024

The Internet of Things (IoT) connects and establishes communication between physical objects from creatures to machinery over the Internet without human involvement that is embedded with sensors, actuators, software, and various other technologies linked together through wired or wireless networks. In the foreseeable future, the application fields of the Internet of Things will increase continuously and dramatically. This paper considers the current progress of the Internet of Things in the real world and presents various tangible applications of IoT in field of agriculture, industries, smart retails, automated systems, smart buildings, automotive IoT, wearable items, transportation, covid -19, e-health, security and intrusion detection. The paper also provides overview of the collaborative applications of the Internet of Things with Big Data, Artificial Intelligence, Machine Learning, Wireless Sensor Networks, Cloud Computing, Data Management, Cryptography and Blockchain to disseminate its applications for a better understanding of the research community to apply IoT in further innovative fields.
Preserve data-while-sharing: An Efficient Technique for Privacy Preserving in OSNs
Authors: Nithish Ranjan Gowda Et al.
Published: January 2024

Online Social Networks (OSNs) have become one of the major platforms for social interactions, such as building up relationships, sharing personal experiences, and providing other services. Rapid growth in Social Network has attracted various groups like the scientific community and business enterprise to use these huge social network data to serve their various purposes. The process of disseminating extensive datasets from online social networks for the purpose of conducting diverse trend analyses gives rise to apprehensions regarding privacy, owing to the disclosure of personal information disclosed on these platforms. Privacy control features have been implemented in widely used online social networks (OSNs) to empower users in regulating access to their personal information. Even if Online Social Network owners allow their users to set customizable privacy, attackers can still find out users’ private information by finding the relationships between public and private information with some background knowledge and this is termed as inference attack. In order to defend against these inference attacks this research work could completely anonymize the user identity.

This research work designs an optimization algorithm that aims to strike a balance between self-disclosure utility and their privacy. This research work proposes two privacy preserving algorithms to defend against an inference attack. The research work design an Privacy-Preserving Algorithm (PPA) algorithm which helps to achieve high utility by allowing users to share their data with utmost privacy. Another algorithm-Multi-dimensional Knapsack based Relation Disclosure Algorithm (mdKP-RDA) that deals with social relation disclosure problems with low computational complexity. The proposed work is evaluated to test the effectiveness on datasets taken from actual social networks. According on the experimental results, the proposed methods outperform the current methods.
A Comparison of Deep Learning Techniques for Glaucoma Diagnosis on Retinal Fundus Images
Authors: R. Geethalakshmi Et al.
Published: January 2024

Glaucoma is one of the serious disorders which cause permanent vision loss if it left undetected. The primary cause of the disease is elevated intraocular pressure, impacting the optic nerve head (ONH) that originates from the optic disc. The variation in optic disc to optic cup ratio helps in early detection of the disease. Manual calculation of Cup to Disc Ratio (CDR) consumes more time and the prediction is also not accurate. Utilizing deep learning for the automatic detection of glaucoma facilitates precise and early identification, significantly enhancing the accuracy of glaucoma detection. The deep learning technique initiates the process by initially pre-processing the image to achieve data augmentation, followed by the segmentation of the optic disc and optic cup from the retinal fundus image. From the segmented Optic Disc (OD)and Optic Cup (OC) feature are selected and CDR calculated. Based on the CDR value the Glaucoma classification is performed. Various deep learning techniques like CNN, transfer learning, algorithm was proposed in early detection of glaucoma. From the comparative analysis glaucoma diagnosis, the proposed deep learning artifact Convolutional Neural Network outperform in early diagnosis of glaucoma providing accuracy of 99.3 8%.
An Enhanced Sampling-Based Viewpoints Cosine Visual Model for an Efficient Big Data Clustering
Authors: Aswani Kumar Unnam Et al.
Published: January 2024

Bunching is registering the item's similitude includes that can be utilized to segment the information. Object similarity (or dissimilarity) features are taken into account when locating relevant data object clusters. Removing the quantity of bunch data for any information is known as the grouping inclination. Top enormous information bunching calculations, similar to single pass k-implies (spkm), k-implies ++, smaller than usual group k-implies (mbkm), are created in the groups with k worth. By and by, the k worth is alloted by one or the other client or with any outside impedance. Along these lines, it is feasible to get this worth immovable once in a while. In the wake of concentrating on related work, it is researched that visual appraisal of (bunch) propensity (Tank) and its high level visual models extraordinarily decide the obscure group propensity esteem k. Multi-perspectives based cosine measure Tank (MVCM-Tank) utilized the multi-perspectives to evaluate grouping inclination better. Be that as it may, the MVCM-Tank experiences versatility issues in regards to computational time and memory designation. This paper improves the MVCM-Tank with the inspecting methodology to defeat the versatility issue for large information grouping. Trial investigation is performed utilizing the enormous gaussian engineered datasets and large constant datasets to show the effectiveness of the proposed work.
Fast and Accurate Calorie Count Prediction from Food Images using a Convolution Neural Network Method
Authors: S. Mohideen Pillai Et al.
Published: January 2024

In the past ten years, with advances in deep learning techniques, automated object recognition has come very near to human levels of accuracy. Fast, automatic, and consistent image-based food calorie calculation is becoming a requirement with the increasing growth of overweight and other lifestyle- related disorders throughout the globe. Accurate and insightful solutions may be provided in the form of a mobile app with the aid of a deep learning-based automatic object recognition system. However, real-time image processing necessitates a high level of processing speed, which is a major consideration for such applications. Although several researchers have looked at estimating calories based on pictures of food, there is currently no image-driven, lightweight, rapid, and accurate food calorie estimation method. In this study, we offer a technique for recognizing common meals captured with a mobile phone camera by using Convolution Neural Networks (CNN) with optimum parameters. Calories and other nutritional information may be deduced from the known food class after the food items have been identified. Our research shows that our suggested method not only guarantees precision but also has the potential to greatly streamline the complex, time-consuming, and labor-intensive processes now used for estimating calorie intake by turning them together into real-time automation techniques.
A Novel Profound Learning-Based System for Breast Cancer Location and Classification Utilizing Exchange Learning
Authors: Satya Pal Singh Et al.
Published: January 2024

Breast cancer is the leading cause of death in Asian women. Breast cancer can be detected early and treated aggressively. In this paper, we propose a new deep learning framework utilizing the index learning concept for breast cancer diagnosis and imaging. Typically, in-depth questions from experts are designed and presented in a specific format. Unlike classical learning paradigms, which are based on parallel development and emergence, the goal is to use the knowledge gained from solving one problem to solve another problem. In this study, we used CNN pretrainers to extract features from images, such as Google Net, VGGNet (Visual Geometry Group Network), and ResNet (Unsupervised Network), which combine the principles of neural networks and social networks. This has already happened. Contains partial information. Use the blunt treasure in the veil. A standard data model was used to evaluate the proposed optical products. Chemical images of different types of breast cancer have been discovered in archaeological finds and have proven useful in educational exploration into the unknown.
Machine Learning and Deep Learning Models for Predicting the Onset of Diabetes: A Pilot Study
Authors: Reehana Sk Et al.
Published: January 2024

Diabetes currently one of the most significant worldwide concerns, and its prevalence is only expected to increase in the future years. In order to monitor glucose levels in the blood and set treatment protocols for diabetes, keeping a regular schedule for checking blood glucose levels is essential. The purpose of widespread adoption of digital health in recent years has been to enhance diabetic healthcare for patients, and as a result, a massive quantity of data has been collected that may be used in the ongoing management of this chronic condition. Deep learning, a relatively new kind of machine learning, is one method that has taken advantage of this trend, and its applications seem promising. In this research, we provide a thorough analysis of how deep learning has been used in the study of diabetes thus far. We conducted a comprehensive literature search and found that this method is most often used in the following settings: diabetes diagnosis, glucose control, and the identification of diabetes-related complications. We have described the most important details regarding the learning models used, the development process, the primary outcomes, and the baseline techniques for performance assessment from the 40 original research publications that we selected based on the search. In the reviewed literature, it becomes clear that several deep learning algorithms and frameworks have outperformed traditional machine learning methods to attain state-of-the-art performance on numerous problems involving diabetes. However, we point out several gaps in the existing literature, such as a dearth of readily available data and a lack of clarity in the interpretation of models. In the near future, these obstacles may be surmounted thanks to the fast advancements in deep learning methodologies which will allow for wider application of this technology in therapeutic settings.
Predictive Analysis of Covid-19 Disease Severity in X-ray images: using Deep Learning Techniques
Authors: Nagamani Tenali Et al.
Published: January 2024

Healthcare systems are evolving in order to deal with the issues of death in human. The most current worldwide pandemic, COVID-19, which first appeared in 2019, has spread throughout the world. Covid sickness is currently one of the leading causes of death in humans. The signs of COVID-19 include fever, coughing, exhaustion, body pains, and shortness of breath. These symptoms can range in severity from moderate to severe. Also possible for some people are sore throats, congestion, runny noses, and loss of taste or smell. The COVID-19 pandemic has prompted researchers to create imaging-based medical treatments, allowing medical staff to detect COVID-19-infected patients more quickly and begin necessary treatments on schedule. The new coronavirus (COVID-19) illness is extremely contagious, thus there are often too many patients waiting in line for chest X-rays. This burdens the radiologists and physicians and has a detrimental impact on the patient's treatment and pandemic management. Due to this highly contagious condition, there aren't as many clinical amenities available, such as hospitals with critical care units and ventilatory machines, it is now crucial to categorise the patients according to their severity levels. Using deep learning techniques, we categorized the individual based on the severity levels of moderate, severe, and extreme if they tested positive for COVID-19. The COVID-19 patient severity divided into three groups: moderate, serious, and extreme, using Convolution Neural Network (CNN) three architecture: VGG19, ResNet-50 and DenseNet201 model that was constructed with an average accuracy of VGG19-89.63%, ResNet-50 with 92.62% and DenseNet201 with 96.4% with the input of chest X-ray pictures.
A Review on Optimization of Microgrid for Rural Electrification
Authors: Mohd Shariq Ansari Et al.
Published: January 2024

Microgrids are a good technique for applying clean and renewable energy by 2030. Goal 7 of the Sustainable Development Goals calls for the improvement of energy operations. Since the majority of people in rural areas lack access to power, rural electrification is extremely difficult. Currently, more than two billion people lack access to energy, which affects their living conditions. A billion people throughout the globe still do not have access to power, making economic progress impossible. Many individuals live in remote, rural areas that are not connected to the national grid. Optimization techniques by offering an analytical framework may play a crucial part in this advancement. Achieve a wide range of economic, social, and environmental goals that are bound by budget, resources, local population statistics, and other considerations. This paper has been divided into basically two subsections. In section one, we begin by compiling. It gives an overview of the microgrid concept and its properties and presents important reviews of the literature on common microgrids and their remote operation localization of electricity in section second we compile various optimization methods of a microgrid. After that, we compile combining intelligent optimization algorithms with adaptive techniques Each issue type is discussed in detail, with a categorization system based on the problem's goal, suggested solution approach, components, size, and area. Finally, we identify upcoming research issues for microgrid improvement.
A Model for Predicting E-Commerce Product Returns Using Hybrid CNN-GRU
Authors: A.Arulmurugan Et al.
Published: January 2024

The aim of this work is to predict the product return rate of e-commerce using deep learning algorithms. A novel hybrid model combining Convolutional Neural Network(CNNs) and Gated Recurrent Units (GRUs) is proposed to predict e-commerce product return rates. The developed model is trained and validated on a large e-commerce dataset with features like consumer demographic informations, product details, transaction and product return details, and consumer feedbacks. The return history of every product is learned and products least and most sold is analyzed. Based on all this analysis the return of products in the future is predicted. The proposed work demonstrates that the hybrid CNN-GRU model outperforms conventional models and  standalone CNN and GRU architectures with an accuracy of 83%. Also focus is made on understanding the features influencing the product returnsthat help firms to make data driven decisions and minimize product returns . This type of predictive models can be applied to enhance the business strategies in making informed decisions enhancing the overall satisfaction of the consumer and improving the revenue of the business.
Artificial Intelligence Framework for Sugarcane Diseases Classification using Convolutional neural Network
Authors: A. Vivek Reddy Et al.
Published: January 2024

In many regions of the world, plant disorders have long been a threat to crop development and agricultural production, negatively affecting the availability of food for people. The best organised sector of agriculture is sugarcane cultivation. It is the first crop that farmers grow because of the ideal conditions for its development. It is closely related to the sugar sector and has a significant impact on the economy of several countries. Of all the crops grown for commercial purposes, sugarcane has the highest production value. In contrast, a different type of diseases can affect the quality and productivity of the crop. Growers can detect some of them by visual inspection of the leaves. Unfortunately, the majority of infections go undetected, causing farmers to suffer significant losses. To reduce the damage caused by an infestation, it is important to determine the type of infestation. So, we proposed a Deep Learning (DL) model that uses images of diseased leaves to train the model to recognise a specific disease affecting the sugarcane plant. In this work, we have used bacterial blight, red rot, red rust and healthy leaf images. The method used two convolutional neural networks to classify the 851 sugarcane leaf images. As a result, Resnet50 achieved the highest accuracy of and 99.70% for binary classification (normal and abnormal images). The trained model achieved its goal by identifying photos of sugarcane and classifying them into classes of healthy and diseased leaves. As a result, this research provides a proposal for using deep learning algorithms to help farmers detect and categorise sugarcane infections. Finally, we have applied an DL visualization technique such as gradient class activation map, occlusion sensitivity, and local interpretable model-agnostic to differentiate and understanding the classification process by highlighting area that is more used for the classification.
Study on Deep Learning Techniques for Finding Suspicious Violence Detection in a Video Surveillance
Authors: B. Venkatesh Et al.
Published: January 2024

Detecting suspicious visual objects is essential to applying automatic violence detection (AVD) in video surveillance. Continuous monitoring of objects or any unusual things is a tedious task. Learning about video surveillance is an emerging research problem in AVD applications. Deep learning is an intelligent and trustworthy technique for detecting or classifying suspicious data objects. It classifies suspicious video frames by modeling specific categories of videos. The current deep models convolutional neural network (CNN), convolutional long-term and short-term memory (ConvLSTM), AlexNet, VGG-16, MobileNet, and GoogleNet, are wildly succeeded in real-time violence detection with the input of video clips. This paper presents the findings of experimental studies for deep models using classification measures to demonstrate the models' efficacy for our AVD application. Benchmarked violence (V), non-violence (NV), and weapon violence (WV) video datasets are used in the experiment to describe the model's performance while classifying suspicious videos for public safety.
Provably Energy Efficiency and Lower Power Consumption Based on HOA in 5G MIMO-NOMA Systems
Authors: Kamil Audah Et al.
Published: January 2024

The rapid expansion of 5G communication networks necessitates improved energy efficiency and reduced power consumption. This article explores the integration of Hybrid Optimization Algorithms (HOA) in 5G MIMO-NOMA systems, aiming to enhance energy efficiency and minimize power usage. The proposed methodology leverages MIMO technology and Non-Orthogonal Multiple Access (NOMA). We introduce a new power consumption model based on HOA, recognizing MIMO-NOMA as pivotal in future wireless communication systems. HOA allows simultaneous service for more users, leading to heightened energy efficiency and reduced power consumption compared to conventional MIMO or NOMA systems. A streamlined user admission scheme is presented, admitting users based on ascending power requirements to meet Quality of Service criteria. Numerical results demonstrate the efficacy of HOA and the power allocation strategy in enhancing energy efficiency and user admission. Comparative analysis shows lower power consumption and approximately a 10% increase in energy efficiency compared to traditional methods and other algorithms like GA, PSO, SPPA, and the water-filling algorithm.
Towards the Definition of Application Developer Persona and Developer Experience Conceptualization
Authors: Abdul Saboor Et al.
Published: January 2024

For decades application developers (ADs) encountered numerous challenges in employing hardware acceleration capabilities as they are difficult to abstract and consume with ease, thus ADs are avoiding such capabilities and eventually ignoring them altogether and fully dependent on the operating system and virtualization vendors to abstract and provision those acceleration features. With the advent of microservices, there is a need to research this topic in order to comprehend ADs requirements and expectations. This paper aims to give additional conceptualization of ADs persona description and experience around the inclusion of software frameworks/libraries among established technology strategy leaders and developers. The qualitative research method was used which led to conducting in-depth individual interviews associated with the domain of application development. These in-depth interviews, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) paradigm, investigate strategic leaders' and technical specialists' intentions to accept and use hardware features. The study finally produced a conceptual framework comprising four aspects that describe ADs persona. The framework provided high-level descriptions of how certain properties can be implemented. The conceptual framework can be utilized by new or established ADs to identify specific traits to focus on. The highlighted features will lead to further studies quantifying their future influence.
ViLiDEx- A Lip Extraction Algorithm for Lip Reading
Authors: Vibhavari Patel Et al.
Published: January 2024

Technology is evolving at an immense speed every day. In the lap of technology, computer vision and machine learning are also growing fast. Many real time applications are running without human interaction just because of Computer vision and machine learning. In this paper, we are using computer vision and machine learning for lip feature extraction for Gujarati language. For this task we have created dataset GVLetters for Gujarati alphabets. We have taken videos of 24 speakers for 33 alphabets of Guajarati language. Face landmark algorithm from dlib is used for deriving ViLiDEx (Vibhavari’s algorithm for Lip Detection and Extraction). ViLiDEx is applied for 24 speakers and 5 alphabets from each class (Guttural, Palatal, Retroflex, Dental and Labial). This algorithm calculates total number of frames for each speaker, keep 20/25 frames as a dataset and removes extra frames. Depending on number of frames, frame numbers divisible by prime numbers are chosen for removal.
Enhancing Biometric Security: A Framework for Detecting and Preventing False Identification
Authors: Hayat Al-Dmour Et al.
Published: January 2024

Biometrics is a technological system that utilizes data to differentiate one individual from another. The biometric framework can be used by government and private organizations for security purposes. This software-based technology helps to look at an individual's data if it is genuine or fake. The study suggested a framework; its goal is to strengthen the development and acceptance of the biometric system. The function of this system is to reduce the applied effort to identify and recognize the quality of the image in less time. This study utilizes three data applications: iris, fingerprint, and face recognition. The approach proposed by the survey uses different features of the images to determine the difference between the original image and the considered sample image. It gives efficient protection against different spoofing attacks. Simulation results show that the high-quality detection application has an average peak signal-to-noise ratio (PNSR) of 89.77. Further, the proposed model effectively detects false biometric identification.
Fuzzy Election based Optimization Algorithm (FEBOA) And Energy Harvesting Possibilistic FUZZYC-Means (EHFPCM) Clustering for EH-WSN
Authors: L. Lakshmaiah Et al.
Published: January 2024

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols.
Nanoparticle Incorporation to Enhance Titanium Alloy Electric Discharge Machining Capabilities
Authors: Mohammed Saleh Al Ansari Et al.
Published: January 2024

The primary objective of this study was to examine the effects of the Powder-blended Micro Electric Discharge Machining (PMEDM) technique on micromachining applications, specifically when sea water is employed as the dielectric medium. Tiny apertures with a width of 200µm were punctured over Ti-6Al-4V plates. In the initial round of experimentation, the machining performance was evaluated by subjecting sea water to various process variables without the inclusion of any other ingredients. The effects of input variables, including electrode material, hole voltage, current, Pulse-on-time, and Duty factor, on the Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), Circularity Error (CE), and Taper Ratio (TR) were analysed by conducting tests in accordance with Taguchi's L18 plan design. The method employed to ascertain the optimal parametric configuration for multi-objective enhancement involved utilising the strategy of soliciting inclination based on similarity to an ideal arrangement. The present study aimed to investigate the effects of foreign materials on the dielectric-based miniature electrical discharge machining (EDM) process in sea water. This was achieved by utilising powders with varying weight concentrations and molecule sizes, including non-conductive (Al2O3), semi-conductive (SiC), and conductive (Al) powders. The experimental setup ensured that the variable boundaries were maintained at their ideal settings. The results indicate that the choice of tool has a notable influence on the functioning of micro EDM when sea water is employed as the dielectric, without the inclusion of extraneous particles. The performance metric of multi-objective execution in PMEDM is influenced by the conductivity of supplementary compounds. An 83.18 percent increase in Material Removal Rate (MRR) was observed when SiC added compounds were utilised in Powder Mixed Electrical Discharge Machining (PMEDM). Conversely, there was a drop in Tool Wear Rate (TWR) by 36.42 percent, Open Circuit voltage (OC) by 21.48 percent, Current Efficiency (CE) by 45.15 percent, and Tool Roughness (TR) by 22.87 percent.
An intelligent Espionage Robot Controlled by Mobile Wireless System
Authors: D Ferlin Deva Shahila Et al.
Published: January 2024

The field of robotics has grown exponentially as a result of the integration of several domains. With advances in technology, a drone, also known as an unmanned aerial vehicle (UAV), can be controlled from 20,000 miles away. Such technologies either use artificial intelligence or radio communication to control their process. Creating one such autonomous robot is the main task. More than a thousand kilometers away, in a distant place, this must be managed. Currently available technologies can only control robots up to 500 yards away. Our communication way is WIFI. These Wi-Fi-connected computers send serial data, and they also communicate with a system using a microcontroller to direct how it operates. The robot senses as well in order to avoid obstacles and determine its location.
The Practical Experience of Financial Investigations in the Republic of Kazakhstan: Problems of Searching and Returning Assets from Abroad
Authors: Seitenov K. K. , Efimov S. V. , Chernov P. L.
Published: January 2024

The article discusses the practical experience, prospects and vectors of development of financial investigations in Kazakhstan, the problems associated with the search and return of assets located abroad. The authors focus on the main obstacles faced by law enforcement agencies in the investigation of financial crimes, as well as existing mechanisms for the return of fraudulently acquired funds from abroad. The article highlights examples of successful and unsuccessful mechanisms for asset recovery, provides recommendations for improving the situation in this area.