bluuebunny/crossref_metadata_2025 huggingface dataset:
“Dynamic Shield: An Algorithm for Secured Data Transmission in Dynamic WBAN Scenarios”
Authors: Dhiraj Sanjay Kalyankar Et al.
Published: January 2024

Healthcare monitoring, sports performance monitoring, industrial wearable sensors, smart retail clothes, and emergency response wearable are all used to evaluate the suggested algorithms. The results show how crucial it is to tailor your choice of cryptographic algorithms to your specific use case and available resources. Data privacy and integrity can be maintained in ever-changing network topologies with the help of the study's suggested strategies for achieving this goal. Building trust and confidence in the dependability of WBAN systems, the suggested algorithms provide powerful security against data breaches, unlawful access, and data loss. With the growing popularity of WBANs in crucial settings, this research provides a solid groundwork for safe and reliable data transmission, opening the door to the easy incorporation of wearable technology in a wide range of uses.
Performance Comparison of Different Load Balancing Algorithms in Cloud Computing
Authors: Satish R. Devane, Ratnadeep R. Deshmukh Ganesh V. Gujar,
Published: January 2024

Cloud computing offers economical, scalable, and instantaneous computing resources to enterprises, allowing them to manage substantial traffic volumes and cater to a multitude of users. But the need for effective load balancing techniques has grown significantly as cloud computing becomes more and more popular. To guarantee the best possible performance, availability, and dependability of apps and services, load balancing is a crucial component of cloud computing. This paper offers a comparative study of different cloud computing technologies and load balancing strategies. We present a performance comparison of software-based load balancing; our analysis compares various service broker policies, such as closest distance, optimized, and reconfigurable, with algorithms such as round robin, throttled, and equally spread. Overall, this paper helps readers understand load balancing mechanisms in cloud computing.
A Proposed Secure Framework for Supply-Chain Management using Blockchain Technology
Authors: Sanjay Jasola Et al.
Published: January 2024

Decentralization increases the performance of the Supply Chain by distributing financial choices more evenly. The technology that is causing the biggest upheaval is the aforementioned challenges that are hampering today's supply chain are addressed by block chain, which is a cost-effective and realistic solution. Many autonomous networks, self-sufficient persons and institutions can emerge and operate in a distributed, dispersed operating environment thanks to block chain. Theblock chain’s data is unchangeable, transparent, and recorded in a distributed ledger that adheres to consensus mechanisms throughout.Hence, the data stored in it cannot be edited and forged i.e., it is trusted if the writer is trusted. Block chain also has its own advantages and setbacks: decentralized network operating in centralized computing devices. Web pages are used to captured changes in original data to malicious data, network synchronization, service outages, and other challenges are some of the issues that could arise in block chain.Thereare several entities and modules involved in the model of this Supply Chain which are tested using an efficient top-down manner in this article. The proposed supply chain management model can be adaptive to various supply chains like food, textile, healthcare and also educational institutions. A comprehensive analysis of the major-cause of numerous threats, susceptibility, and hazards is assisted on the system of supply chain management as imitated in different scenarios.
Evaluation of Bearing Fault Detection on Different _K-Folds using Deep Learning Ensemble Models
Authors: Deep Prakash Singh Et al.
Published: January 2024

One of the most crucial parts of contemporary machinery and industrial equipment is the induction motor. Therefore, it is essential to create a fault diagnosis system that can identify induction motor problems and operating circumstances before they become serious. In this study, an induction motor's defect diagnosis is carried out in three different states, including normal, rotor fault, and bearing fault. The suggested fault diagnostic system is also described, along with a GUI. The experimental findings support the suitability of the suggested approach for rotor and bearing defects in induction motor diagnosis. A GUI for defect diagnostics was also created and used in a real-world setting. We have used Chi-Square method for high score attributes values. For the normal, rotor fault, and bearing fault states of induction motors identified by DBN, CNN, SNN, SVM and RF respectively, the fault detection system's accuracy in the actual world. In the experiment, we find Algorithms model-II, K-Folds (5, 10 & 15) , Accuracy (%), Training loss, Validation loss value for RF-SVM-CNN are 89.2, 0.260013, 0.304936 for k fold 5, 98.4, 0.155960, 0.154133 for k-fold 10 and 98.3, 0.155759, 0.144127 for k- fold 15 respectively.
Which Factors Can Contribute to the Success of Environmental and Animal Protection Projects in Donation-based Crowdfunding? A Neural Network Model
Authors: Chang Jiang Et al.
Published: January 2024

The crowdfunding industry has developed rapidly in recent years, the existing research shows that crowdfunding can help in many fields such as entrepreneurship, creative products, or donations. Due to global meteorological issues, more and more people are paying attention to the environment and animal protection. However, fundraising in these areas has been the biggest problem, the emergence of donation crowdfunding (DCF) can alleviate this dilemma. Currently, in academia, there is still less research focused on crowdfunding for environmental and animal protection. This paper aims to study the factors influencing the successful financing of environmental and animal protection projects in the DCF.

This paper analyses 700 DCF environmental and animal protection projects in China as samples, and creatively introduces financial transparency scoring indicators. Through binary logistic regression, financial transparency was found to be the most critical positive factor affecting project success. At the same time, donors receive NPO-initiated projects well, and the number of donors can also positively impact the results. However, the excessive description of the projects can have the opposite effect. This study also introduced a neural network model, and found that the neural network model can optimize the discriminant accuracy of the traditional binary logistic regression model.
Assessing Equity in Healthcare Facility Resource Allocation in Yemen: An Entropy-TOPSIS Analysis
Authors: Adel. A. Nasser Et al.
Published: January 2024

The objective of this study is to investigate the fairness of healthcare facility resource allocation in Yemen and provide insights and suggestions for promoting fairness in their allocation. The Health Resource Concentration and Entropy-TOPSIS models were used to assess the fairness of healthcare facility resource allocation, taking into account the geographical population distribution in Yemen. According to the study, there are disparities in the allocation of healthcare resources in Yemen based on both geographical concentration and population. The governorates of Amanat Al Asimah, Aden, Ibb, Al Mahwit, and Taizz were found to have a higher level of fairness in facility resource allocation than other governorates based on geographical concentration. However, the allocation of health facilities by geographical concentration was deemed unfair, with a very low concentration of health facilities in Al Maharah, Hadramaut, Socotra, Al Jawf, and Shabwah. Additionally, the governorates of Socotra, Amran, Hadramaut, and Al Maharah were found to have a fair allocation of health facilities that can meet the needs of the concentrated population. On the other hand, Sa'ada, Hajjah, and Hudaydah were found to have a shortage of at least 86% of health facility types relative to the population size. The Entropy-TOPSIS model found that Marib, Socotra, Al Maharah, Aden, and Hadramaut had the highest comprehensive health facility resource concentration scores. In light of these findings, the study suggests enhancing the fairness of healthcare facility resource allocation in Yemen by improving coordinated development.
A Technique Using Machine Learning to Anticipate and Differentiate Between Biodegradable and Non-Biodegradable Waste
Authors: Menaka. S Et al.
Published: January 2024

Urban waste has become a significant issue for planners due to the challenges of identifying and disposing of it. The rise in urban populations has resulted in a corresponding increase in waste and garbage. To address this issue, in this study, the researchers introduce a concrete approach that utilizes a Deep Learning (DL) framework to perform waste sorting at its basic level. In contrast to recognizing objects of a specific category, waste can have various characteristics such as color, shape, material or size making it challenging to detect. To overcome this, the authors proposed a material-based deep learning model called Smart-Bin, which employs an Improved Faster Recurrent Convolution Neural Network (IFRCNN) approach to differentiate between biodegradable and non-biodegradable waste. The aim of this study is to evaluate the performance of various IFRCNN models such as AlexNet, ResNet, InceptionNet, and VGG-16 together with the hardware system implemented for waste classification within the bin, the suggested technique demonstrated superior performance compared to other models. The InceptionNet Neural Network achieved remarkable precision rates of 98.15% and a training dataset loss of 0.10, while achieving 96.23% precision and a loss of 0.13 for the validation dataset.
Risk Prioritization using A FUZZY BASED Approach in Software Development Design Phase
Authors: Tabrez Khan Et al.
Published: January 2024

The success of a software project's objective is directly proportional to the degree to which it satisfies all of the stakeholders' concerns regarding the project's requirements, including the budget, schedule, and overall performance. Risks can occur throughout the software development lifecycle (SDLC) phases and affect every phase. The design phase of the SDLC yields an overview of the software and can be defined as the software's blueprint. Different types of software have their own unique design phases and have different types of risks. With the high number of interacting components, complex systems have a greater propensity to be more volatile, which increases the risk. It is necessary to prioritize the risks in order of their severity levels. The issue at hand is the lack of effective methods to prioritize and mitigate the risk. Recent studies have suggested several methods for prioritizing risks, but it is clear that few of these have been implemented. These methods are overly complicated, time-consuming, prone to inconsistency, and challenging to put into practice. This paper proposes a novel Fuzzy-based approach to risk prioritization in the software design phase using MATLAB software. Fuzzy-based models have been shown to be more accurate than other techniques when using standard datasets to prioritize risks. Fuzzy-based methods that have been proposed take into account the characteristics of risks by modelling those characteristics as fuzzy
Design and Implementation of a Self Adaptive Architecture for QoS (SAAQ) in IoT based Wireless Networks
Authors: Durgesh Kumar Keshar Et al.
Published: January 2024

The rapid growth of Internet of Things (IoT) applications has made ensuring quality of service (QoS) in wireless networks essential. This paper presents the design and implementation of a Self-Adaptive Architecture for QoS (SAAQ) in IoT-based wireless networks, using the NS-2 simulation tool as a foundation for analysis and evaluation. The SAAQ framework is carefully tailored to meet the dynamic demands of IoT applications, enabling real-time adjustment of QoS parameters such as packet delivery ratio, throughput, end-to-end delay, packet loss ratio, energy consumption and routing overhead. By integrating with NS-2, a simulation tool in network research, we conduct extensive simulations and experiments to evaluate the SAAQ's effectiveness in diverse IoT scenarios. This paper explores the adaptability and scalability of the SAAQ architecture and results of experiments reveal the practical benefits of the SAAQ in enhancing QoS in a simulated IoT application over other methods such as AODV, AOMDV, and LEACH.
Feature Selection Techniques for Wood Density Prediction in Forest Dataset
Authors: Jeyabharathy Sadaiyandi Et al.
Published: January 2024

Feature selection becomes important, especially in data sets with a large number of variables and features. It will eliminate unimportant variables and increase classification accuracy and performance. Because of the increasing growth of data in numerous industries, some data is high-dimensional and contains essential and complex hidden linkages, posing new problems to feature selection: i) How to extract the underlying available relationships from the data, and ii) How to apply the learnt relations to improve feature selection? To address these issues, we use the six feature selection approach as a pre-processing step in the analysis to avoid over fitting and potential model under performance. Which can learn and apply the underlying sample relations and feature relations for feature selection. This study compared six feature selection approaches (Pearson Coefficient, Correlation matrix, Variable Importance, Forward selection, and Backward Elimination) for determining the decomposition level of forest trees. Our trials clearly provide a comparative evaluation of the Wrapper approach from several angles. Furthermore, we compare the dataset result with critical attributes to obtain the highest percentage accuracy. The experimental results show that the wrapper technique outperforms all other methods in all experiment groups.
A Design of Improved Trust-based Clustering Approach to Enhance the Energy Computation in Secured Internet of Things (IoT)
Authors: Satish Kamble Et al.
Published: January 2024

The Internet of Things (IoT) is the trending area that occupies maximum of the applications of intelligent communication. The nodes in the IoT system are located in the monitoring environment to attain stable and reliable communication. In between the nodes and the sink, similar data is sensed and transmitted at maximum of the period and that leads to high energy consumption. On the other hand, it increases the security issues of the network. So the main objective is reducing power utilization and increasing the IoT network security. In this paper, we suggest an improved trust-based clustering approach to enhance energy computation and security (ITCES-IoT) in IoT networks which can able to reduce energy consumption and increase the security among the nodes in the network. We first introduced an effective system model that can able to reduce the transmitting and receiving power during communication. Secondly, the initial cluster setup phase, LEACH-based cluster head (CH) selection phase, and re-clustering principles are discussed. Then, the trust calculation is established for both the cluster members (CM) and the CH through direct and indirect trust calculation processes followed by the best solution of path selection is performed between the CM and the sink node. We constructed Bad mouthing attacks, Ballot stuffing attacks, and detection mechanisms with a Fast Entropy algorithm. Finally, intra-cluster and inter-cluster communication are elaborated in the communication phase. Through this process, the nodes and CHs can able to perform effective communication with minimum power utilization, and the nodes with the transmitted information are secured from the external environment. At the end stage, we validate our proposed ITCES-IoT approach in certain scenarios in network simulator NS2 and their performance analysis includes the parameters of packet delivery ratio, network throughput, and packet loss and energy consumption. Then the final outcomes are compared with the base methods such as ECET-IoT, DRTP-IoT, and RCDA-IoT. From the results it is understood that the proposed ITCES-IoT outperforms the baseline methods in terms of delivery ratio and throughput and as well it consumes less power at the time of data transmission.
Artificial Intelligence and Deep Learning-Based System Design for Diabetic Retinopathy Classification
Authors: Monelli Ayyavaraiah Et al.
Published: January 2024

One of the biggest causes of avoidable blindness throughout the world is diabetic retinopathy (DR). There is a significant unmet need to test all diabetes patients for DR, and many instances of DR go undetected and untreated. In order to automate DR screening, this research aimed to create reliable diagnostic technologies. In order to reduce the pace of vision loss, it is important to refer eyes suspected of having DR to an ophthalmologist for further assessment and treatment. The primary goal of this research is to improve the classification accuracy for Diabetic Retinopathy (DR). In this script, we present a new neural network model for DR forecasting. The suggested model's accuracy in identifying DR phases was measured against that of regular and ensemble-based models. Various benchmark datasets, including MESSIDOR, IDRID, and APTOS, are used in the studies. The suggested DRPNN algorithm outperformed the competition in experiments assessed using industry-standard criteria.
Secure and Decentralized healthcare applications using Blockchain
Authors: Govinda K Et al.
Published: January 2024

Before the Internet period started, we had Usenet wherein the clients needed to run their very own servers so as to impart and as web went along 10 years after the fact one needed to have their site on the server they claimed and needed to purchase a DNS area to point to their site making a decentralized framework, however this gave security which nobody expected in those days it was likewise unbelievably costly and consequently came the facilitating administrations which advanced a progressively concentrated framework which was without a doubt increasingly productive and less expensive around then. Brought together frameworks were increasingly advantageous to clients yet as the quantity of clients continued duplicating, it gave huge capacity to the organizations that claim these incorporated frameworks. These enterprises claim heaps of client information and control the web with the ability to impact all that we see and do on the web. The ongoing advancements in the Blockchain innovation have allowed to bring back the decentralized frameworks and it is right now being utilized by digital forms of money and in fintech yet with more research on blockchain could change different mechanical parts of our life including our administration and the ebb and flow human services framework
Diabetic Prediction Using Hybrid Smote-Tree Big Data Classification with Artificial Neural Network
Authors: Praveenkumar K S Et al.
Published: January 2024

Diabetes is one of the worst illnesses now plaguing humanity. The condition is caused by the body's abnormal reaction to insulin, a vital hormone that transforms sugar into the energy required for the normal functioning of daily living. In addition to increasing the chance of developing kidney disease, heart disease, and retinal eye disease, nerve damage, and blood vessel damage, diabetes causes serious consequences in the body. This research provides diabetes prognosis based on hybrid SMOTE-TREE large data categorization utilizing Artificial Neural networks (ANN). Artificial Neural Networks deliver promising results for nonlinear data. Hence ANN is picked for creating the model to predict diabetes among numerous ML (machine learning) techniques. The goal is to develop a decision support system to predict and diagnose diabetes with maximum accuracy, given the parameters. The parameters are set such that the best accuracy is obtained.
Design and Development of a High Performance Hybrid Patch Antenna with Enhanced Bandwidth for Wireless Communication
Authors: Neera Agarwal Et al.
Published: January 2024

In the current scenario of modern antenna design, patch antennas have emerged as a fundamental component in modern wireless technology due to their compact size, low profile, and versatility in diverse applications. However, to meet the ever-increasing demands for higher data rates, extended bandwidth, and improved radiation characteristics, innovative design approaches have become essential. In conclusion, this research paper showcases the current state of modern patch antenna design, emphasizing the significant improvements in performance and versatility. The research  work introspects into the design of a hybrid patch antenna with a modified cutting-edge design employing an  optimization techniques resulting in a high performance Patch antenna with better bandwidth and high gain in comparison to its counterparts. The results obtained insights provided in this study will be valuable for researchers, engineers, and designers in the field of antenna technology, paving the way for the development of more efficient designs in hybrid antenna technology.
Optimizing Expert Rankings with Multiple Regression Analysis
Authors: A. Abdul Azeez Khan Et al.
Published: January 2024

The primary goal of this research is to establish a methodology that gives significant weight to an expert's qualifications and experience in the expertise ranking process. This methodology aims to enhance the effectiveness of expert identification by taking into account an expert's background and credentials, thus yielding more realistic expert rankings. To achieve this, we incorporate the details of an expert's qualifications and experience into the evaluation process by assigning assumed values, which are then integrated with their expertise level. These combined factors are subsequently utilized as inputs for a multiple regression analysis to generate an optimal ranking of experts. By emphasizing the significance of experience and qualifications in the ranking process, we can significantly improve the precision of our expert ranking mechanism. Our approach employs multiple regression analysis to identify the most suitable subject expert for user query transformation.
Machine Learning-Based Optimization Method for the Oxygen Evolution and Reduction Reaction of the High-Entropy Alloy Catalysts
Authors: Jagannath Jijaba Kadam Et al.
Published: January 2024

In recent times, high-entropy alloys (HEAs) have found application in heterogeneous catalysis, capitalizing on their vast chemical potential. Yet, this extensive chemical landscape presents significant challenges when attempting a comprehensive exploration of HEAs through traditional trial-and-error approaches. Therefore, the machine learning (ML) approach is offered to appearance into the catalytic activity (CA) of countless sensitive sites on HEA surfaces in the oxygen-lessening response (ORR) and oxygen evolution reactions (OER). In this research, a Density Functional Theory (DFT) with a supervised ML model is assembled and founded on the gradient boosting regression (GBR) algorithm that predicted the O2 adsorption energies with a high overpotential of all surface sites on the two HEAs. Initially, the HEAs Co-Fe-Ga-Ni-Zn and Al-Cu-Pd-Pt offer a framework for adjusting the composition of disordered multi-metallic alloys to regulate the activity and selectivity of the reduction of oxygen to extremely reduced compounds. This attains generalizability, high accuracy and simplicity with the proposed technique. For fine-tuning such features, HEAs provide a huge compositional space. Consequently, the research reports the custom of the Bayesian optimization model based on HEA active compositions to suppress the formation of Oxygen (O2) and with strong O2 adsorption to favour the lessening of O2. The GBR approach is applied to build a highly accurate, easily generalizable, and effective ML model. The proposed work is analysed using Python software. The findings show that the separate charities of correlated metal atoms close to the responsive site are mixed to form the adsorption energy, which is clear from a thorough analysis of the data. It is suggested that a highly effective HEA catalyst composed of Co-Fe-Ga-Ni-Zn and Al-Cu-Pd-Pt be exploited, which is an effective method for further enhancing the ORR CA of potential HEA catalysts. An instruction manual for the logical design and synthesis of HEA catalysts' nanostructures is provided by the proposed research.
A Novel Approach for Single-Source 3-Phase 7-level Boost Inverter
Authors: Sapana Sharma Et al.
Published: January 2024

In this article, a 3-phase inverter with a voltage gain of three demonstrated via the toggle capacitor concept.It synthesizes a line voltage waveform with seven levels. The most salient feature of this topology is that a single input DC locate is necessaryfor the 3-phase implementation.Each branch of the proposed topology requires eight power electronics switches and 2condenser. The opposite of the current reach their highest across the energyoutlet is equal to the value of the input DC voltage. The capacitors are all self-balanced at all modulation indices. The proposed approach minimizes the cost function to 5.429, as the proposed topology exhibits better topological characteristics mainlythe boosting-factor, overall-voltage and component..Topology with similar features, however, has a const function of 7.333. In addition, the suggested topology is highly modular, so the amount of pole energyscalescould be increased by further adding switched capacitors, and the same arm structures can be used for all phases. However, an important limitation is that the pole voltage is unipolar, and therefore redundant states in phase are not available, although they are readily available in line voltage. The 7-level topology and modulation procedure are verified by result discussion, which represents that with a DC energy of 100 V, a 3-phase voltage with about 300 V could be obtained in equal steps of 100 V, while the pole voltage is a unipolar 4-level peak value 300V in equal steps of 100V.
IoT Safeguarding in Saudi Tourism Sector: Crafting a Preliminary Security Model for Enhancing Cyber Resilience
Authors: Sarah Alghamdi, Et al.
Published: January 2024

Incorporating the Internet of Things (IoT) has transformed technological landscapes, facilitating seamless communication across diverse devices and systems. However, this increased connectivity exposes critical sectors, including government and tourism, to elevated cybersecurity risks. There is a lack of knowledge regarding how organizations within the Saudi tourism sector address the cybersecurity risks associated with IoT systems. While much of the existing IoT literature concentrates on adopting IoT systems, a better understanding can be attained by proposing a preliminary research model encompassing the most significant factors influencing IoT security and related cybersecurity attacks. Despite limited empirical research on IoT security adoption in the Saudi tourism sector, this study seeks to address this gap. Motivated by this concern, this research investigates IoT security among Saudi organizations in the government tourism sector by developing a research model.  inspired by the Technology Acceptance Model (TAM) literature. The model incorporates a total of eight factors (privacy, confidentiality, data integrity, access control, availability, trust, IoT standards and policies, and IoT Awareness) and seven cybersecurity attacks (denial of service (DoS & DDoS), replay attack, eavesdropping attack, man-in-the-middle (MiTM) attack, spoofing attack, Sybil attack, and physical attack) identified from various literature sources. The proposed research model is a valuable tool for understanding IoT security in Saudi tourism, offering guidelines for organizations considering introducing IoT security measures. These guidelines highlight specific factors that tourism organizations should consider, enhancing the likelihood of successful IoT security adoption in the tourism context. Additionally, this study encourages IoT researchers to replicate the research in another industry sector within Saudi Arabia or other countries, particularly within the Arabian Gulf region.
Harnessing Convolutional Neural Networks for Histopathological Breast Cancer Classification.
Authors: Thyagaraj T, Et al.
Published: January 2024

Recent advancements in Convolutional Neural Networks (CNNs) have significantly supported the field of breast cancer discovery using medical imaging. An improvised DenseNet architecture for the classification of histo-pathological breast cancer images is explored in this work. Leveraging the effectiveness of DenseNet in capturing intricate patterns through dense connectivity, our improvised architecture aims achieve high accuracy and efficiency of classification. The model integrates novel features such as optimized bottleneck layers and attention mechanisms, contributing to improved feature extraction and classification capabilities. The improvised DenseNet produced a accuracy of 93.39% on the breakhis dataset. A summary of key findings and future research directions, emphasizing the need of custom CNN models in breast cancer detection is provided.
Implementation of Passivity-based Control of Four-Leg Inverter for Transformerless Three-Phase Solar Photovoltaic Systems
Authors: N. Uday Kumar Et al.
Published: January 2024

This study explores the application of a passivity-based control technique (PBC) with an artificial neuro-fuzzy inference system (ANFIS) in a grid-connected Solar Photovoltaic (SPV) generating system for estimating reference grid currents. To optimise the tracking of the maximum power point (MPPT) between the solar photovoltaic (SPV) system and the DC bus, a DC – DC boost converter (BC) is utilised. This converter is connected to a four-leg, three-level neutral-point-clamped converter (4-leg 3L-NPC), interfaced with a three-phase, four-wire distribution system. The four-leg 4-leg 3L-NPC of the SPV generating system with Space vector pulse width modulation technique (SVPWM) is utilised for reducing common-mode voltage (CMV), leakage current (LC), reactive power for zero voltage regulation, power factor correction, load balancing, and the elimination of load harmonic currents in the proposed distribution system.The performance analysis of the proposed system is conducted on the Matlab/Simulink platform. Additionally, a comparative analysis is presented, contrasting the proposed system with a 3-phase 4-leg T-type neutral-point-clamped converter (TNPC) employing the PBC technique followed by ANFIS.
Artificial Intelligence Enabled Methods for Human Action Recognition using Surveillance Videos
Authors: Boddupally Janaiah Et al.
Published: January 2024

Computer vision applications have been attracting researchers and academia. It is more so with cloud computing resources enabling such applications. Analysing video surveillance applications became an important research area due to its widespread applications. For instance, CCTV camera are used in public places in order to monitor situations, identify any theft or crime instances. In presence of thousands of such surveillance videos streaming simultaneously, manual analysis is very tedious and time consuming task. There is need for automated approach for analysis and giving notifications or findings to officers concerned. It is very useful to police and investigation agencies to ascertain facts, recover evidences and even exploit digital forensics. In this context, this paper throws light on different methods of human action recognition (HAR) using machine learning (ML) and deep learning (DL) that come under Artificial Intelligence (AI). It also reviews methods on privacy preserving action recognition and Generative Adversarial Networks (GANs). This paper also provides different datasets being used for human action recognition research besides giving an account of research gaps that help in pursuing further research in the area of human action recognition.
Segmentation and Classification of Arecanut Bunches before harvesting
Authors: Dhanesha R. Et al.
Published: January 2024

In the agriculture sector, arecanuts are an extremely valuable crop. The price of an arecanut depends on its stage of ripeness. As a result of a lack of expertise in judging the maturity level of arecanut bunches before harvest, farmers often lose profit. Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality.  Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality. Therefore, accuracy in segmenting arecanut bunches is vital for automated maturity level identification. In proposed work S-channel, Cr-channel and Pr-channel of HSV, YCbCr and YPbPr respectively color models are used to segment arecanut bunches. Three color features (i.e., mean of an arecanut bunch image on red, green, and blue bands), and two texture features (i.e, correlation, and entropy) were used in classification procedure. A random forest classifier was employed to classify maturity levels of arecanut bunch. This experiment uses a dataset of 1017 images of arecanut bunches to assess the segmentation performance of each color model. As a result of the experiment, it has been concluded that the S-channel of the HSV color model was effective in segmenting arecanut bunches from input images. The proposed methodology effectively classifies arecanut bunch maturity levels with an accuracy of 87.80%.
Real Time Face Mask Detection using Deep Learning Approach
Authors: Shikha Singh Et al.
Published: January 2024

In response to the need for face masks in preventing dangerous illnesses like COVID-19, this paper is centered around deep learning-based face mask identification. Leveraging powerful libraries such as TensorFlow, Keras, and OpenCV, our objective is to create a reliable and effective system capable of discerning individuals wearing face masks from those who are not. This innovative approach holds immense potential for applications in public health monitoring, security systems, and critical public spaces where adherence to face mask policies is of utmost importance.To ensure practical usability, we deploy the trained model in real-time scenarios, utilizing webcams or video streams. The system efficiently processes frames, swiftly detecting and classifying faces with or without masks, and promptly providing feedback or alerts to users or surveillance systems.This paper highlights how well TensorFlow, Keras, and OpenCV work together to create a reliable and accurate face mask detection system. The amalgamation of deep learning, image processing, and real-time capabilities facilitates seamless monitoring of face mask adherence, significantly contributing to public safety and health initiatives across various domains.
Exploring Software Development Change Analysis with an Emphasis on Requirements
Authors: Syed Adnan Afaq Et al.
Published: January 2024

Most software requirements are not definitive of the development process. Rapid changes in user expectations, market conditions, and company practises all need regular updates to software. Requirement change management is a crucial and difficult component of every software development project. Project failure or cancellation often occurs because of requirements changes. Requirements Change refers to requirements that are added, removed, or amended during the system development life cycle. Requirements Change requires additional work in the design phase, which boosts the cost of developing the system, lengthens the time required, and reduces system quality. The paper investigates research efforts in the topic of requirement change and helps in determining the study's purpose. Various Requirement Change Management concepts and approaches are provided, and numerous activities are conducted to mitigate the effects of requirement changes. The study emphasizes on causes, attributes, prioritization of changed requirement, framework for RCM and Change Impact Analysis. This study briefly describes all the possible fact and figure about requirement change.  The study includes various phases of requirement change management such as Requirement Elicitation, Requirement Change Identification in Requirement Document using Two Phase Requirement Document Comparison Algorithm, Prioritization of Changed Requirement using Fuzzy approach, Interdependency analysis and change impact analysis on various software project parameters such as time, cost and human resources.
Using Tiktok as a Live Marketing Strategy to Attract Female Consumers' Interest in Purchasing Cosmetic Products in Indonesia
Authors: Nur Fita Sari Et al.
Published: January 2024

This research examines TikTok's role as a direct marketing strategy to engage female consumers in purchasing cosmetic products in Indonesia. It employs a quantitative approach, surveying 250 active female TikTok users who interacted with cosmetic content. The study investigates various factors, including TikTok user recommendations, shopping experiences, content interactivity, shopping guidance, product visibility, influencer impact, customer engagement, and repurchase intention. Respondents rate these factors on a Likert Scale. The analysis, carried out utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), intends to uncover how these variables affect individuals' intentions to make repurchases

 

This study aims to offer an understanding of how TikTok serves as an effective marketing platform for cosmetics targeted at female consumers in Indonesia.. The PLS-SEM approach is chosen for its ability to handle complex relationships in the data, including non-linear ones. By examining the relationships and conducting statistical analysis, this study aims to contribute to our understanding of how TikTok influences consumer behavior in the cosmetic industry.
MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop
Authors: Sujit R Wakchaure Et. al
Published: January 2024

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.
Analysis on Security Vulnerabilities of the Modern Internet of Things (IOT) Systems
Authors: Rohith Vallabhaneni, Abhilash Maroju, Sravanthi Dontu Srinivas A Vaddadi,
Published: January 2024

The IoT, or Internet of Things, has quickly grown in popularity as a means to collect data in real-time from any and all linked devices. These networked physical objects can exchange data with one another via their respective sensor technologies and have their own unique identifiers. Insightful data analytics applied to the obtained information also presents a substantial possibility for many organisations. Embedded devices, authentication, and trust management are all areas where the Internet of Things has shown a significant security hole. This study delves into the problems with the Internet of Things (IoT), covering topics such as its privacy and security, its vulnerability, its analytics at the moment, the impending ownership threat, trust management, IoT models, its roadmap, and its security issues. It then offers solutions to these problems.
A Dual- Band Microstrip Array Antenna with Defected Ground Structure
Authors: N V Koteswara Rao, P Chandra Sekhar Upender Rao B,
Published: January 2024

This paper objective is to construct and analyse a dual-band inset-fed microstrip array antenna for 4.77 GHz and 5.09 GHz dual band frequency with a defective ground structure (DGS) plane. An inset-fed microstrip patch antenna array's ground plane could have H shaped slots etched out of it to permit operation in the second frequency band (4.77 GHz), which is a design concept. To achieve this goal,a wide parametric analysis is applied to select the placements of etched slots that have the desired impedance bandwidth, high gain in the desired bands, and mismatching in the undesirable bands. The proposed dual-band antenna that acts as a monopole-like antenna and it can be used for wireless communications.
Applications of Deep Learning Approaches to Detect Advanced Cyber Attacks
Authors: Rohith Vallabhaneni, Abhilash Maroju, Sravanthi Dontu Srinivas A Vaddadi,
Published: January 2024

The number and sophistication of cyber attacks have grown, making it tougher to detect and prevent them using traditional security technologies. Improving cyber threat identification and response has been greatly enhanced by deep learning, a subset of machine learning. Learn how to spot advanced cyberattacks with the help of Deep Learning algorithms in this article. The proposed approach collects, categorises, and arranges network traffic data using convolutional neural networks (CNNs) and intermittent neural networks (RNNs). Combining the RNN with the CNN allows us to capture temporal dependencies and derive spatial properties from the network data. In order to find the most important qualities for classification, the proposed method also incorporates a feature selection stage. To demonstrate that the proposed system outperforms signature-based and example AI systems in terms of exactness, accuracy, review, and F1-score, we conduct an exhibition survey using various datasets. An effective tool for improving cyber defences, the proposed method can detect zero-day and previously unknown attacks.
Predicting Diabetes Risk Using an Improved Apriori Algorithm
Authors: Arpit Solanki, Et al.
Published: January 2024

The data mining is able to analyze data and recover the valuable insights from the data. These insights are used for serving in different applications. In this context different data mining algorithms has been developed among them frequent pattern mining has an essential role. In this paper, the frequent pattern mining technique has been implemented for analyzing the diabetic risk. In this context, the popular diabetic dataset has been obtained. Then, the preprocessing has been done on dataset for cleaning the dataset. Next, an encoding process has been developed to transform the dataset. This transform dataset is an effort to deal with the continuous values using the frequent pattern algorithm. Further a modified apriori algorithm has been employed to understand and establish the relationships between diabetic attributes. The experiments have been carried out and theexperimental performance of the improved apriori algorithms has been measured. Additionally a comparison has also been performed with three popular frequent pattern mining algorithms. According, to the performance, we found that the proposed apriori algorithm is efficient and accurate algorithm to predict the diabetic risk.
Unmasking the Threat: Exploring Effective Techniques for Investigating ATM Malware Through Digital Forensic Analyses
Authors: Kiranbhai R Dodiya Et. al
Published: January 2024

Hackers have developed new techniques to directly breach a system or device, responding to the surge in numerous cybercrimes. Criminals frequently attack automated teller machines (ATMs) because of the vulnerabilities they present. ATM security must monitor and investigate these attacks because ATMs may contain and manage enormous sums of cash, making them good targets for hackers. Because of this, ATM security needs to monitor and investigate these assaults. Because automated teller machines may hold and handle significant quantities of money, the security at ATMs needs to monitor and analyze assaults of this kind. In the present study, using Studio to analyze ATM malware is essential to identifying malware signatures and behaviours that can improve ATM security. Banks and ATM manufacturers must implement specific measures to prevent ATM malware attacks. Based on present research on ATM malware analysis, researchers have uncovered critical insights that are highly beneficial for those seeking to investigate Malware aimed at breaching automated teller machines (ATMs). It is an invaluable tool for conducting such investigations. This paper will serve as a means of sharing insightful findings and aiding others in deepening their understanding of this crucial topic.
Polish Criminal Law in Relation to a Time of War – Selected Issues
Authors: Kozłowska-Kalisz Patrycja
Published: January 2024

The article presents selected regulations of Polish criminal law (also from a historical perspective) relating to the regulations of criminal liability related to the time of war. Some of these regulations were established as a consequence of the war, and their purpose was in particular to regulate the issue of criminal liability for behavior undertaken during and in connection with the war. Others were introduced “in case” of war so that they could only be updated in a war situation. The concept of time of war can be found in the provisions of Polish criminal law (especially military criminal law) as a hallmark of types of crimes – as a modal circumstance of the time of committing the crime and as a qualifying hallmark, determining a more severe punishment.