bluuebunny/crossref_metadata_2025 huggingface dataset:
THE PAINTED FOX OF IVAN FRANKO AND THE PRINCIPLES OF QUANTUM MECHANICS (psychological and physical transformations of Mykit the Fox in the tale of Ivan Franko as an explanation of the trends of the functionality of the principles of quantum mechanics in the macro world)
Authors:   Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, Ukraine, Kholod O. М.
Published: January 2024

The article considers the tendencies of three principles of quantum mechanics for the micro world (uncertainty principle, additionality or complementarity principle and superposition or superposition principle) as having similarities with the tendencies of analysis of processes occurring in the macro world. The hypothesis is proved during the analysis of the fairy tale “Painted Fox” by Ivan Franko. The aim of the research was to identify, search for and describe the psychological and physical transformations of Fox Mykyta in Ivan Franko’s fairy tale as an explication of the tendencies of the functionality of the principles of quantum mechanics in the macro world. Among the research methods were chosen methods of analysis, synthesis, content analysis on the units of “image” and “theme”. We have chosen the following procedure as the research method: search for publications related to the problem analyzed by us; implementation of content analysis by unit “image” and “theme”; classification of identified fragments of the text of the fairy tale “Painted Fox” according to three criteria: “Compliance with the principle of uncertainty”, “Compliance with the principle of complementarity” and “Compliance with the principle of superposition”. Based on the analysis, it was concluded that the trends reflected in the three principles of quantum mechanics for studying the processes of the micro world (uncertainty, principle of additionality or complementarity and the principle of superposition or superposition) are clearly fixed in macro world processes.
EDUCATION OF FREE PERSONALITY IN THE LIGHT OF PEDAGOGICAL IDEAS OF IVAN FRANKО
Authors:   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Zakharia А. S.
Published: January 2024

The article attempts to consider and analyze the views of I. Franko on the development of the education system in Ukraine. The ideas of I. Franko on the right of children and young people to study in their native language, to acquire knowledge on humanistic, moral-ethical and national-patriotic grounds have been proven. The relevance of this problem is emphasized, as well as the importance of the pedagogical views of I. Franko in the modern world.
Privacy Preserving Data Mining using Jaya based Genetic Algorithm
Authors: Srutipragyan Swain Et al.
Published: January 2024

Privacy protection has emerged as a key concern in the field of data mining because of the rise in the sharing of sensitive data across networks among organizations, governments, and other parties. For   knowledge extraction from these huge set of data, association rule mining is used to analyze the patterns of data. For a variety of optimization issues encountered in the real world, evolutionary algorithms (EAs) offer efficient solutions. The available EA solutions in the privacy-preserving area are limited to specific issues like cost function evaluation. Here, a JAYA based Genetic algorithm has been proposed for privacy preservation.“JAYA” means victory a word from Sanskrit origin..This algorithm doesn't need any parameters that are specific to it and it moves towards best solution avoiding the worst. Hence the name Jaya. Jaya based genetic algorithm is applied to original dataset of chromosomes. Privacy preservation is achieved by comparing the support of original dataset of chromosome with the simulation output.
Optical Fiber Bragg Grating Filter for Wavelength Division Multiplexing (WDM) Applications
Authors: Pinky Khundrakpam Et al.
Published: January 2024

A Fiber Bragg grating is an aperiodic or periodic disorder of the effective index of refraction in the optical fiber core, having nanometres range period. For short periods of the index modulation, the disorder in index of refraction perturbation induces the light reflection in a limited wavelength range, for which condition of Bragg is satisfied. Incidence of a broad-spectrum light onto one fiber end containing a fiber Bragg grating, induces reflection of the portion of the light corresponding to wavelength of the Bragg grating to the input end, while the remaining light reaches the other end. Optical wavelength division multiplexing (WDM) networks employed in long distance and metro areas; several nodes are present to distribute the signal. The optical add-drop multiplexer (OADM) is the significant equipment which is responsible for separation of distinct wavelengths and redirection of the linked signals across the network to the corresponding users. The operating principle of the optical add-drop multiplexer deems an accurate filtering operation, provided by Bragg gratings. In this paper, optical fiber Bragg grating filter is designed and analysed for Wavelength Division Multiplexing (WDM) application.
Activity Reimagined: The Effects of a Tailored Exercise Protocol on Post-Myocardial Infarction Recovery
Authors: Poovishnu Devi Thangavelu Et al.
Published: January 2024

Introduction: Global health issues include cardiovascular diseases, notably CHD. Continuing ischemia causes irreparable heart muscle damage, or MI. An important cause of death in India is CHD. Lifestyle worsens heart disease. The Structured Exercise Protocol (SEP) in this study targets post-MI rehabilitation with resistance and aerobic training. Results may improve survivors' quality of life by affecting post-MI therapy. Exploration follows the study guides post-MI care.

Objectives: The purpose of the study is to evaluate how a structured exercise protocol affects a person's exercise tolerance and activity level after a myocardial infarction. The research, which involves 45 participants and a 12-week intervention, compares the results of traditional workouts with the Structured Protocol in an effort to ascertain the efficacy of the protocol in enhancing cardiovascular health and general well-being in post-MI people.

Result: The outcomes of the study show that those who underwent the Structured Exercise Protocol after a myocardial infarction experienced notable improvements in their exercise tolerance and activity status. The efficiency of the regimen in boosting cardiovascular recovery is highlighted by statistical analysis. The results call for more research on cardiac rehabilitation techniques and show how customized exercise interventions might improve post-MI care regimens.

Conclusion: The study concludes that exercise tolerance and activity status are considerably improved in post-myocardial infarction individuals by the Structured Exercise Protocol. The results demonstrate the effectiveness of the regimen as a helpful intervention for cardiovascular rehabilitation and point to its potential to improve long-term outcomes for this population.
An Efficient Fuzzy Based Multi Level Clustering Model Using Artificial Bee Colony For Intrusion Detection
Authors: Battini Sujatha Et al.
Published: January 2024

Network security is becoming increasingly important as computer technology advances. One of the most important components in maintaining a secure network is an Intrusion Detection System (IDS). An IDS is a collection of tools used to detect and report network anomalies. Threats to computer networks are increasing at an alarming rate. As a result, it is critical to create and maintain a safe computing environment. For network security, researchers employ a range of technologies, including anomaly-based intrusion detection systems (AIDS). These anomaly-based detections face a major challenge in the classification of data. Optimization algorithms that mimic the foraging behavior of bees in nature, such as the artificial bee colony algorithm, is a highly successful tool. A computer network's intrusion detection system (IDS) is an essential tool for keeping tabs on the activities taking place in the network. Artificial Bee Colony (ABC) algorithm is used in this research for effective intrusion detection. More and more intrusion detection systems are needed to keep up with the increasing number of attacks and the increase in Internet bandwidth. Detecting developing threats with high accuracy at line rates is the prerequisite for a good intrusion detection system. As traffic grows, current systems will be overwhelmed by the sheer volume of false positives and negatives they generate. In order to detect intrusions based on anomalies, this research employs an Efficient Fuzzy based Multi Level Clustering Model using Artificial Bee Colony (EFMLC-ABC). A semi-supervised intrusion detection method based on an artificial bee colony algorithm is proposed in this paper to optimize cluster centers and identify the best clustering options. In order to assess the effectiveness of the proposed method, various subsets of the KDD Cup 99 database were subjected to experimental testing. Analyses have shown that the proposed algorithm is suitable and efficient for intrusion detection system.
An Efficient Approach to Forecasting the NIFTY-50 Indian Stock Market's Daily Closing Price with Artificial Neural Networks
Authors: Kuljinder Singh Bumrah Et al.
Published: January 2024

The most lucrative area of the financial industry is stock market trend forecasting. The incorporation of machine learning has improved the accuracy, accessibility, and dependability of stock market forecasts. This study compares and contrasts several machine learning methods, including Support Vector Machine (SVM), Random Base Pairing (RBF), Multi-Layer Perceptron (MLP), and Single Layer Perceptron (SLP). The unique features of each method are also examined separately. Of these methods, the MLP algorithm produced better results. The inquiry explores the relationship between changes in the dollar exchange rate, the performance of NIFTY 50, Foreign Institution Investors' Gross Purchase (FII inflow), and Foreign Institution Investors' Gross Sale (FII outflow). For the study, daily average closing prices for the NIFTY 50, FII inflow, FII outflow, and Dollar are taken into account. The study covers a period of 5 years from April 1, 2018, to March 31, 2023. The NN Toolbox in Matlab is used in the analysis to evaluate the correlation between NIFTY 50, FII inflow, FII outflow, and Dollar exchange rates. A strong correlation has been found by the data analysis between Dollar values, FII inflow, FII outflow, and NIFTY 50.
An Efficient Encryption System on 2D Sine Logistic Map based Diffusion
Authors: Akshay Chamoli Et al.
Published: January 2024

An optimal cryptographic model is proposed, enabling the feature of 2D sine logistic map-based diffusion algorithm. The 2D sine logistic map process is merged with the algorithm as it has the ability to provide random number generator as well as to overcome blank. The previous existing models based on image encryption use to work on raw images but without alteration for the process of confusion and diffusion. The main disadvantage as the nearby pixel values for an image always remains similar. This issue is resolved by a Pseudo random generator process which is based on key stream that alters pixel value. Furthermore 2D sine logistic map based diffusion process has shown an improvement in the key sensitivity and the complex relationships that use to get developed between cipher and test image.2D sine logistic map with diffusion method used to keep pixels intact with each other to such an extent as even a single bit modification in the intensity value of an original image pixel will lead to a huge change in most of the pixels of the cipher and thus makes the model very sensitive to make any changes in the pixel value or secret key for an image.  As seen and analyzed with a variety of test results that strategic model used for encryption can easily encrypt the plain image into a cipher of a random binary sequence.
Navigating Post-Covid Economic Recovery: Women's Role in Indonesian Development
Authors: Muhammad Nasor Et al.
Published: January 2024

The aftermath of Covid-19 has deeply affected economic landscapes worldwide, and the role of women within this paradigm is crucial for understanding human development. This study delves into the impact on women's advancement by examining their treatment in education, health, and economic sectors as indicators of development achievements. The UNDP has developed measures like IPG and IDG, assessing gender development components across various dimensions. Conducted as qualitative research, this study draws data from primary sources—observations, interviews, and documentation—from three Indonesian provinces: West Sumatra, Lampung, and East Java. Results indicate a mixed scenario post-Covid-19. Despite the pandemic's impact, West Sumatra saw continuous growth in Gender Empowerment Index and Gender Development Index (GDI) from 2020 to 2022. However, the IDG remained below the national level, while the IPG surpassed national standards. Lampung Province witnessed an increase in IPG from 2020 to 2022. Conversely, East Java experienced a decline in IDG in 2021, followed by a subsequent increase in 2022. IPG in East Java displayed relative growth during the pandemic. Across these provinces, the Gender Empowerment Index lagged behind national growth, while IPG for West Sumatra and East Java exceeded it, with Lampung province falling below. These findings underscore the intricate dynamics shaping post-Covid-19 gender roles in economic recovery in Indonesia's various regions.
Proto-oncogene Protein Sequence Classification using RNN and CNN with Attention Mechanism
Authors: M.Vijayalakshmia Et al.
Published: January 2024

The fundamental tool for uncontrolled tumour progression is the lack of regulatory capacity of tumour suppression genes (TSG) and mutations in proto-oncogenes (OG). Even though tumour is a diverse complex of several diseases, discovering possibilities of genes connected to OG  activity by computational research can aid in the development of medications that specifically target the disease. Attention mechanism in Deep learning has recently become an innovative approach for classifying protein sequences. The attention-based approach can offer a trustworthy and understandable method that aids in overcoming the existing difficulties in describing deep neural networks for classifying protein sequences. In this study, we classify proto-oncogenes (OG) with the help of CNN, Bi_LSTM and Bi_GRUwith attentionmechanisum. of all the three attention mechanisms, Bi_LSTM significantly performs far better than the other two approaches and achives F1-Score upto 97.3% and it is 3% more traditional ML Random Forest approach.
Remote Monitoring, Managing & Disease Detection of Plantation using IoT framework
Authors: Aruna Gawade Et al.
Published: January 2024

Living in a country having an Agrarian economy, one of the fundamental objectives of this paper is to cater to the urban class, who because of their busy schedules don’t indulge in gardening activities. This IoT Application enables the user to remotely access and irrigate the garden, become cognizant of its current state, monitor the surrounding environment of the garden to steer clear of potential danger and obtain graphical representations of the data collected from various sensors. Further, this system allows the user to obtain a live video feed of the plants, obtain information on the ideal requirements of the plant and detect the possibility of any diseases through leaves. At the application level, a web-app is designed and implemented that can be installed on users phone, thereby bestowing the users with the power to access the system at their fingertips.
Self-Clocked Shift Registers Utilizing 90 nm CMOS: Design, Analysis and Insights
Authors: Jami Venkata Suman Et al.
Published: January 2024

This paper presents an efficient approach to designing and analyzing four bit shift register utilizing self-clocked D flip-flops as integral storage components. The use of internal clock generation within these flip-flops obviates the need for external clock synchronization. These specially designed flip-flops incorporate a reduced number of transistors in comparison to conventional designs, leading to notable enhancements in power efficiency, packaging density, and operational speed. The implementation of self-triggered D flip-flops facilitates the creation of various shift register configurations, including Serial in Serial out (SISO), Serial in Parallel out (SIPO), Parallel in Serial out (PISO), and Parallel in Parallel out (PIPO). These registers not only occupy a smaller die area but also exhibit diminished power consumption and heightened operational speed when contrasted with standard counterparts. The design and simulation procedures are executed using the Microwind tool and a 90 nm CMOS technology.
Employing the Powered Hybridized Darts Game with BWO Optimization for Effective Job Scheduling and Distributing Load in the Cloud-Based Environment
Authors: Manish Chhabra Et al.
Published: January 2024

One of the most frequent issues in cloud computing systems is job scheduling, which is designed to efficiently reduce installation time and cost while concurrently enhancing resource utilisation. Limitations such as accessible implementation costs, high resource utilisation, insufficient make-span, and fast scheduling response lead to the Nondeterministic Polynomial (NP)-hard optimisation problem. As the number of combinations along with processing power increases, job allocation becomes NP-hard. This study employs a hybrid heuristic optimisation technique that incorporates load balancing to achieve optimal job scheduling and boost service provider performance within the cloud architecture. As a result, there are many less problems with the scheduling process. The suggested work scheduling approach successfully resolves the load balancing issue. The suggested Hybridised Darts Game-Based Beluga Whale Optimisation Algorithm (HDG-BWOA) assists in assigning jobs to the machines according to workload. When assigning jobs to virtual machines, factors such as reduced energy usage, minimised mean reaction time, enhanced job assurance ratio, and higher Cloud Data Centre (CDC) resource consumption are taken into account. By ensuring flexibility among virtual computers, this job scheduling strategy keeps them from overloading or underloading. Additionally, by employing this method, more activities are effectively finished before the deadline. The effectiveness of the proposed configuration is guaranteed using traditional heuristic-based job scheduling techniques in compliance with multiple assessment metrics.
Customer Churn Prediction in Telecom Industry Using Deep Learning Techniques
Authors: Vinston Raja R Et al.
Published: January 2024

In recent times, the telecommunications request has beenveritably competitive. The cost of retaining telecom guests is lower thanattracting new guests. A telecom company must understand client churn through client relationship management. Therefore, CRM analyzersare demanded to prognosticate which guests will change. In this design, aclient abandonment vaticination model was proposed that uses the Decision Treealgorithm, Random timber algorithm, and Deep literacy algorithm to identify thechurn guests. In Deep Literacy, a Multilayer Perceptron Neural Network has beenused to produce the vaticination model. The performance of all the algorithmswill be compared and estimated in terms of delicacy. The advanced performancevaticination model can be used in the telecom sphere for prognosticatingwhether the guests will churn or not. By knowing the significant churn factorsfrom client data, CRM can ameliorate productivity, recommend applicableelevations to the group of likely churn guests grounded on analogous getspatterns, and exorbitantly ameliorate marketing juggernauts of the company.
Smart and Secure Blockchain Structure to Track Vehicle Record-keeping in the Sultanate of Oman
Authors: Mani Joseph P Et al.
Published: January 2024

In the country of Sultanate of Oman, the Royal Oman Police (ROP) is responsible for carrying out the Inspection, Registration/Renewal, Transfer of ownership, Licensing of a vehicle and the Investigation of traffic accidents. The traditional system used for storing this information does not guarantee whether any information has been tampered with or manipulated. Having a tamper-proof methodology like Blockchain to capture this important information in a distributed ledger register can enable all the participants in the Registration Chain (Supplier of the vehicle, Owner of the vehicle, various Government Agencies and public at large) to gain confidence, build trust and authenticate records that are maintained by the centralised agency. In this paper, a groundbreaking solution built on Hyperledger Besu, a permissioned Ethereum blockchain platform, is introduced that offers a revolutionary approach to vehicle registration and management. It ensures secure and immutable vehicle registration, utilizing IPFS for document storage and ERC-20 tokens to track vehicle history. This Ethereum-powered system facilitates transparent ownership transfers, comprehensive vehicle history tracking, streamlined insurance renewals, seamless vehicle inspections, real-time tracking, and blockchain-based verification, all contributing to heightened efficiency and trust in vehicle processes for the Omani government. By integrating blockchain technology, this solution addresses the challenges of data integrity, security, and transparency that have long plagued the vehicle registration and transfer processes in Oman. This paper highlights the potential of blockchain to revolutionize governmental services and improve the overall efficiency and trustworthiness of vehicle-related transactions, ultimately benefiting both the government and citizens alike.
Ideal Keyword Match in a Big Data Application Using Keyword Aware Service Recommendation Method
Authors: Tumma Susmitha Et al.
Published: January 2024

The big data movement additionally influenced service recommender systems. The emergence of alternative providers has created a big research issue in providing clients with relevant suggestions for services they want. Service recommender systems have proven to be helpful tools that help users manage the multitude of services at their disposal and provide pertinent recommendations. Because the quantity of customers, services, and other online information is growing exponentially, service recommender systems function in a "Big Data" context. This poses serious challenges for these systems. In this work, we address these difficulties by contributing the following: This makes use of a collaborative filtering algorithm that is user-input driven. Keywords extracted from user reviews reflect their preferences here. Additionally, we apply it to Hadoop, a distributed computing framework that builds on Map Reduce for processing. by applying a collaborative filtering process that is user-based. In the proposed system, we are using a user-based Collaborative Filtering method. It also has similarities to the existing system. We consider both customer reviews and company rankings. We provide KASR, a method for keyword-aware service recommendation. Key words in KASR serve as indicators of users' preferences, and recommendations are produced by a user-based Collaborative Filtering algorithm. A domain thesaurus and keyword-candidate list are provided to help better understand the preferences of the customers. The active user indicates their choices by selecting keywords and preferences from the keyword-candidate list.
A Hybrid Stacked CNN Model with Weighted Average Ensembling for Effective Face Recognition
Authors: Rishi Sharma Et al.
Published: January 2024

The discipline of computer vision has given a lot of attention to facial recognition. Automated face recognition is extensively employed in various practical scenarios, including systems for streamlining immigration checkpoints, intelligent monitoring of visual data, and authentication of personal identity. Depending on the situation, it may be divided into the two separate duties of facial verification and face identification. This study proposed a hybrid stacked CNN model for face recognition system. The models have mastered the art of making their own inferences. The models are then combined to predict a class value using a cutting-edge method called weighted average ensembling. A more accurate estimate should be produced by the new assembly process. Pre-trained CNN models are used in our proposed method: AlexNet, Resnet50V2, and VGG-19, Yolov5, VGG-16 and ResNet50. When applied to Tufts dataset images, our suggested model successfully achieved 98.05% accuracy. We have also used the Discrete Wavelet Transform (DWT) method for denoising, SegNet for Image segmentation for better performance of the model proposed.
A Channel Model Suitable for Employing Efficient Beam Steering on Tropospheric Communication Links
Authors: Amit Garg Et al.
Published: January 2024

Tropospheric communication systems find extensive use in both civil and military applications for long distance point to point communication. 21st century warfare requires high performance tropo systems capable of supporting high data rates for real time communication between various fighting elements. Most of the research and development for enhancing performance of tropo systems, has been in minimizing propagation loss due to fast-fading of channel. Limited progress has been made to overcome slow, diurnal and seasonal, changes in troposphere, which lead to considerable signal attenuation. Beam steering can be used to minimize slow fading loss in tropospheric links, enhancing their performance. Transmitter and receiver antenna beams should be dynamically steered in order to point to the tropospheric heights that are most conducive for propagation at any given time, for receiving maximum power. This paper discusses prominent tropospheric channel models and examines their suitability for beam steering. Dinc’s ray-based model can be used for employing beam steering in tropospheric links. It employs a ray-based approach for determining powers received due to signal propagation at different tropospheric heights using real world data of atmospheric parameters obtained from NASA’s LIDAR experiment. However, Dinc’s model needs certain modifications for exact representation of tropospheric turbulence and its propagation characteristics. A modified ray-based channel model has been presented in this paper, derived by making required changes in Dinc’s model. Simulation results, showing a comparison of transmission loss obtained from modified model and Dinc’s model with widely used ITU-R P.2001 model, are presented in the paper for validation. The paper also presents calculation of received power for different heights using modified model, validating its suitability for beam steering. Real world atmospheric parameters have been obtained from Indian Meteorological Department (IMD) for validation process. Received powers obtained for different heights using modified model have been compared to determine the most favourable heights for signal propagation. Knowledge of favourable heights for different times of a day in different seasons, can be used to steer transmit and receive antenna beams towards the favourable heights, at any given time and day, resulting in efficient beam steering.
Inspired Symbol-based Authentication against Dictionary attacks Using ML Algorithm
Authors: J.Balaraju Et al.
Published: January 2024

Verifying an individual's identity before granting them access to a linked device, an internet service, or another resource is known as user authentication. The significance of authentication lies in its ability to safeguard data, apps, and networks for companies by limiting access to their protected resources to only authorized individuals or processes. This paper used the popular Big data technology Apache Spark for storing, and processing large data and proposed a novel authentication framework. A viable replacement for conventional alphanumeric passwords, bio-metric and facial authentications with dynamic symbol selection as an authentication. This authentication is tested in the Apache spark cluster which is the most distributed system. In these methods, SHA512 cryptography is used in several ways and comparison is done using existing authentication and machine learning algorithms. The straightforward authentication scheme and applied Apache Spark distributed system with 10 nodes produced the best results.
Optimal Prediction and Disease Severity Classification of Proteomic Survival in Pre and Post-Covid-19 Using Hybrid Machine Learning Approach
Authors: Jagannath Jijaba Kadam Et al.
Published: January 2024

Uncertainty surrounds the underlying mechanisms of the severe COVID-19 disease of 2019. The capability to detect COVID-19 through artificial intelligence techniques, particularly deep learning, will help to do so in the early stages, which will increase the likelihood that patients around the world will recover rapidly. The load on the healthcare system globally will be relieved as a result. Several thousand plasmas and serum proteins from COVID-19 patients and symptomatic controls are longitudinally analysed in this study to identify non-immune and immune proteins associated with COVID-19. The development of predictive models thus involves taking into account the topological variations across networks from different scenarios (survivors vs. non-survivors). As a result, the study's test subjects, who weren't included in the machine learning (ML) training, had high prediction accuracy. This study successfully predicted the existence of critically ill (CI) patients both before and after COVID-19 by using an MLM built on a synonymic network that incorporates measurements of several proteins. A rise in some acute phase and inflammatory proteins (IP) with time (e.g. ITIH3, SAA1; CRP, SAA2, LBP, SERPINA1, and LRG1) is related to the danger of death after COVID-19, while an upsurge of kallikrein (KLKB1), kallistatin (SERPINA4), thrombin (F2), Apo lipoprotein C3 (APOC3), GPLD1, and the protease inhibitor A2M, is associated with survival. The same clinical symptoms, such as dry cough, fever, squatness of breath, and others, are linked to both severe and critical patients. The lesion outlines are then retrieved from the COVID-19-contaminated regions after the entropy texture features have been extracted using a Gray-level co-occurrence Matrix (GLCM) to confirm the infected regions (IR). Further, the study implemented a variety of features using CT images with a CNN-based Inception V3 model for selection algorithms to filter significant features. Finally, construct a model of transfer learning (TL) using the VGGNet16 model which could capture and further classify the disease severity. Based on Matlab software, the suggested work is assessed. With a compassion of 96.7% and specificity of 98.2%, the results demonstrate that VGGNet16 is the most suitable TL model to identify COVID-19, nonetheless, it also exceeds the most advanced methods at the moment. The clotting system and accompaniment cataract are home to the bulk of proteins in the forecast model with high significance. This work shows that plasma proteomics (PP) can result in prognostic predictions that vastly outperform the present prognostic markers in critical care, respectively.
Use of Cryptocurrencies and Intelligent Systems in Agriculture
Authors: Neeta Maitre Et al.
Published: January 2024

The digital transformation is remarkably advanced due to the decentralized way of digital transactions using cryptography. The term coined is “Cryptocurrency” and has triggered the world in the fastest pace. The intelligent systems promptly complement it. The use of cryptocurrency is majorly seen in NFT (Non Fungible Token) domains and universally addressed through the digital wallets and websites. Like every other domain, agriculture and agricultural products can also be seen as a major sector to be acquired by the new age technology of cryptocurrencies. The field thus is accompanied by intelligent systems such as the use of artificial intelligence(AI) , Internet of things(IoT) in computer systems. The agricultural domain and its technological progress can be witnessed through the adoption of precision agriculture in various countries. This research paper focuses on the intelligent perspective of the agricultural field and also dives through the sustainability achievement by the same. The paper concludes with the discussion on sustainable agriculture as a new paradigm.
Addressing the Deficit: An Analysis of Boardroom Diversity Gaps and Progress toward Global Governance Standards in Bahrain
Authors: Marwan Mohamed Abdeldayem, Et al.
Published: January 2024

this study aims to identify and describe the level of board diversity in Bahraini firms listed on Bahrain Bourse, also known as the Bahrain Stock Exchange (BSE), and estimate the disparity between the employed diversity in Bahrain compared to international standards. The research seeks to answer two key questions: (1) what is the description of board diversity in Bahraini firms listed on BSE? (2) What is the gap in board diversity levels between Bahraini firms listed on Bahrain Stock Exchange (BSE) and international practices? Hence, content analysis was employed to collect the required data from the annual reports of 114 publicly listed service firms between 2018 and 2022. Through data analysis, the study assessed the representation of diverse directors on the boards of Bahraini firms, focusing on gender diversity. The analysis revealed that Bahraini boards continue to be predominantly composed of male directors. Female directors accounted for only 5 percent of the total directors, with an average of 0.35 female directors per board. Based on the findings, the study recommends a review and revision of laws and regulations pertinent to the board of directors, including corporate law and the corporate governance code in Bahrain. These revisions are necessary to promote and ensure greater board diversification within Bahraini firms. The study goes beyond mere description and estimation by addressing the practical implications of the findings. By recommending a review and revision of laws and regulations related to the board of directors, including corporate law and the corporate governance code, the study offers actionable suggestions to promote greater board diversification in Bahrain.
Enhancing Retail Strategies through Apriori, ECLAT& FP Growth Algorithms in Market Basket Analysis
Authors: Harshali Patil, Et al.
Published: January 2024

"Market basket analysis" is a method employed in data mining to discover items that are commonly bought together by customers in a retail store. It is a crucial tool for retailers to understand consumer purchasing behavior and to improve their sales and marketing strategies. In this research paper, we present a comprehensive study on market basket analysis using three popular algorithms: Apriori, ECLAT, and FPGrowth. The paper begins with a brief synopsis of market basket analysis and the techniques adopted for itemset mining. We then introduce the dataset used in this study, which consists of real-life transaction data collected from a retail store. Next, we perform a thorough evaluation of the Apriori, ECLAT, and FPGrowth algorithms in terms of their computational time and the quality of the association rules generated. The results show that the FPGrowth algorithm is the fastest of the three algorithms, while the Apriori algorithm generates the most comprehensive and high-quality association rules.In addition, we also present a comparison of the performance of these algorithms that involve different assessment criteria like support, confidence, and lift. Our study highlights the importance of selecting the appropriate algorithm for market basket analysis depending on the specific requirements and constraints of the task. The paper concludes with an analysis on the limitations and future directions of research in this area. Overall, our study provides insights into the strengths and weaknesses of the Apriori, ECLAT, and FPGrowth algorithms and functions as a valuable resource for professionals and researchers in the field of market basket analysis.
Rain Streaks Removal from Rainy Images Using Convolutional Neural Networks
Authors: M. Manju Et al.
Published: January 2024

Image processing is very important thing to improve the contrast and enhance the degraded images of real world. The rainy images are blurred and contain unclear structure of the objects for that apply deep learning based method to recover the rainy image objects. In this paper, remove the rainy streaks from rainy image and improve the contrast of the rainy image for that apply deep learning method of RainyNet is based on the Convolutional Neural Network. The RainyNet is made up of the encoder-decoder structure is constitute the detail rectify the network to find out the final clear structure. Here, DarkRain trained synthesised dataset is take over to testify the proposed method to produce good results than state-of-art methods. The proposed RainyNet is achieved better results than existing methods in terms of PSNR and SSIM.
Alzheimer Disease Detection of 3D-CNN with SE-Net Model using SVM Classifier
Authors: R. Hemalatha Et. al
Published: January 2024

Alzheimer disease is a fatal progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. In this work we proposed two methods. First, proposed connected median filter using PSO feature extraction from MRI images and Analysis of Alzheimer’s diseases state by using 3D-CNN based SE-Net. In the first phase, the algorithm first normalizes and removes skull from the MRI images. Connected median filter using Particle Swarm Optimization algorithm is used to partition the image into white matter (WM), grey matter (GM) and black holes (BH). The relevant diagnostic features are extracted from the segmented image component. The classifier is trained by the training data to predict the test data. The features are defined to construct classification model by using Support Vector Machine with Squeeze- Excitation block. Here, database contains total of 1000 images which are resized into 350 × 350 without loss of information. Deep Learning demands large number of images and its strength was increased as per requirement by augmentation technique. In the first phase of the method takes 1000 images of different features are selected to train SVM classifier and the accuracy obtained is 98.37%  and contribution of this work is classification of images into categories such as Alzheimer (AD) and normal. First phase of work emphasized program specific applications to extract features.  In the second phase the CNN multiple layers which are studied from lower level to the higher-level image characteristics.
Predicting Customer Churn in E-Commerce Using Statistical and Machine Learning Methods
Authors: Vidya Rajasekaran Et al.
Published: January 2024

This research work aims to develop prediction models and analytical insights to overcome customer churn issues through data-driven approaches. The attrition rate of consumers in e-commerce is a significant issue requiring effective retention strategies. A novel methodology is proposed comprising data preprocessing, using statistical analysis techniques developing the model and carrying out tailored retention strategies. The model is used to identify crucial churn influencers and propose practical recommendations for enhancing consumer retention. The significance of this work lies in its potential to allow e-commerce ventures with insights with the intention of price savings strategies, enhanced revenue measures, and better consumer fulfillment. This research will influence the e-commerce business by facilitating evidence-based methods for reducing customer turnover and increasing long-term customer value. The resulting accuracy of the proposed model using Logistic Regression results in  87 percentage of accuracy which is a good metric to assess overall model performance. The Kaplan-Meier curve is used to check the survival probability of consumers and identify consumers more likely to churn over time.
Analysis of Quality of Experience Evaluation Framework for RPL Protocol in Mobile IoT Environments under Manhattan Grid Mobility
Authors: Kablian K Et al.
Published: January 2024

This research paper introduces a novel and advanced framework for assessing the Quality of Experience (QoE) implications associated with the utilization of the Routing protocol for low power and lossy networks (RPL) in the context of the Internet of Things (IoT). The study undertakes a thorough investigation into RPL's performance under the influence of Manhattan grid (MG) mobility scenarios, addressing a notable gap in current research. By meticulously incorporating essential Quality of Service (QoS) metrics like average packet delivery ratio (PDR), average power consumption (Avg_P), and average inter-packet time (Avg_IPT), the framework enables an in-depth evaluation of QoE. The uniqueness of this work lies in its incorporation of a comprehensive dependency matrix (DM) and the subsequent application of a dependency function (DF) that comprehensively captures the multifaceted aspects of the system's perceived quality.

Beyond its methodological innovation, this research enhances our comprehension of RPL's adaptability through the shift from static to dynamic environments. Furthermore, the study systematically explores various scalability levels, contributing novel insights into how RPL performs across diverse IoT scenarios.Based on the Quality of Experience (QoE) analysis, it can be deduced that the network effectively maintains the performance of a static model even under conditions of MG scenarios. In the static model, the RPL's performance was measured at 67.73%. However, when exposed to the MG mobility model, its performance decreased to 42.93%. Given that RPL is primarily optimized for static models and considering its static model performance as a reference benchmark, it manages to retain approximately 63.38% of the static model's performance when subjected to the MG mobility model.
A Transient Enhanced Capacitor-less Low dropout Regulator Using 180nm CMOS technology
Authors: Maliha Naaz Et al.
Published: January 2024

Demand for system on-chip (SoC) designs and portable electronics has risen quickly in recent years. The dual loop architecture serves as the foundation for the capacitorless LDO described in this research. In order to overcome the difficulties in establishing stability, including quick transient performance and minimal voltage spikes under rapid loadcurrent fluctuations, the regulator uses two feedback loops. The suggested design runs with a 0-100 pF capacitive load and does not require a decoupling capacitor to be placed at the output. A 0.18 µm CMOS technology is used for performing the simulations on the design. The Capacitor-less LDO is given an input voltage of 1.0 - 1.4V, and it gives an output of 0.9 V. Line and Load Regulation obtained are 0.821 mV/V and 0.1122 mA/V respectively. The Capacitor-less LDO has a phase margin of 87.55o , making it more stable whereas phase margin of Conventional LDO is just 43.78o . The transient response of Capacitor less LDO is enhanced succesfully. The overshoot and undershoot of the Capacitorless LDO are 16.38 mV and 16.9 mV respectively while the conventional LDO shows 27 mV and 33.6 mV respectively. Settling time of Capacitor-less LDO is 1.54 µs, which is much better than settling time of conventional LDO, which is 8 µs
Designing and Executing Security Solutions for IoT-5G Environment
Authors: Pratik Shah, Et al.
Published: January 2024

The integration of the Internet of Things (IoT) with 5G networks presents a transformative approach to modern connectivity solutions, yet it introduces significant security challenges. This paper focuses on the design and implementation of robust security techniques tailored for IoT-5G systems. We commence by analyzing the unique security requirements posed by the confluence of IoT devices and 5G technology, emphasizing the need for advanced security protocols to address increased data volumes, device heterogeneity, and potential vulnerabilities. Subsequently, we propose a comprehensive security framework that includes innovative encryption methods, intrusion detection systems, and secure communication protocols specifically developed for the IoT-5G environment. Our approach integrates multi-layered security mechanisms to ensure data integrity, confidentiality, and availability across the network. The effectiveness of our proposed techniques is demonstrated through a series of simulations and real-world deployments, showcasing significant enhancements in security and resilience for IoT-5G systems. This study contributes to the field by providing a practical and scalable security solution, paving the way for secure and reliable IoT-5G integration in various applications, from smart cities to industrial automation.
Improving Data Security in Public Cloud Storage with the Implementation of Data Obfuscation and Steganography Techniques
Authors: Sarika Hemant Gadekar, Et al.
Published: January 2024

Cloud computing is a widely used distribution paradigm for delivering secure information services over the internet. The advantages of cloud computing include the capacity to remotely access one's data from any location, eliminating concerns over data backups, as well as the establishment of disaster recovery and business continuity facilities. Nevertheless, cloud computing gives rise to apprehensions over the appropriate management of information and interactions by cloud service providers, user organisations, and governments. Cloud computing has become an increasingly popular choice for both big organisations and individuals seeking cost-effective access to a wide range of network services. Typically, individuals' information is kept on a public Cloud, which is accessible to everybody. This basic gives rise to several concerns that are contrary to the adaptable services offered by cloud providers, such as Confidentiality, Integrity, Availability, Authorization, and others. Currently, there are several choices available for safeguarding data, with encryption being the most favoured one. Encryption alone is insufficient for adequately safeguarding the sensitive information of many users. Additionally, the encryption and decryption procedure for each every query requires a greater amount of time. Furthermore, it is not advisable to just prioritise user-centric thinking, since users relinquish direct control over their data once it is uploaded to Cloud premises. Given this reality, it is important to contemplate the security of users' vital information on the Cloud server. This may be achieved by the use of the crucial method known as obfuscation. In order to alleviate the load on the Cloud server and provide sufficient security for user data, we suggest an approach that combines both strategies, namely... The thesis explores the concepts of obfuscation and encryption. If the files or documents need security, the user data may be encrypted. The Cloud's DaaS service is protected utilising obfuscation methods. By using a dual-pronged strategy, the suggested technique provides enough protection for anonymous access and ensures the preservation of privacy, even while dealing with information stored on Cloud servers. The objective is to provide a robust integrity checking method, an enhanced access control mechanism, and a group sharing mechanism. These improvements seek to reduce the workload and foster a higher degree of confidence between clients and service providers.