bluuebunny/crossref_metadata_2025 huggingface dataset:
Mining as a Threat to Ensure the Economic Security of the Irkutsk Region
Authors: Derevtsova Irina , Gryaznov Alexander , Sementsov Konstantin , Chalov Evgeny
Published: December 2023

In the last decade due to technological progress, such trends as globalization, the creation and development of new markets, which, in turn, created the latest economic processes and changed radically the traditional monetary economy, have intensified. Cryptocurrencies have now become a new form of money. This «new» money has become very attractive both for investors (due to their anonymity and a high degree of profitability) and for state financial control bodies. Special attention to cryptocurrencies on the part of state control bodies is due to the fact that the new means of payment has not been sufficiently tested in practice yet and is fraught with a significant number of threats, both for the country's financial market and for the economic security of the state as a whole. In this regard, there is an urgent need to consider the main problems associated with the use of cryptocurrencies, as well as to determine their impact on the sustainable development of the national economy of the Russian Federation and the socio-economic development of its territories. In the process of writing of the scientific article, it was revealed that the development of cryptocurrency and an increase in the share of mining in the money circulation system of the Irkutsk region carries a serious economic threat and affects the energy, financial, banking, and tax spheres of the region. At the same time, the shortcomings of the current legislation have been identified, which require methodological, legal and organizational study.
Correlation between Socio-Psychological Adaptability and Tolerance to Uncertainty among Psychology Students
Authors: Aksenova Elizaveta , Sineva Olga
Published: December 2023

The problem of correlation between socio-psychological adaptability and tolerance to uncertainty among graduates from psychology faculties of higher institutions has obvious theoretical and practical relevance. The variety of approaches to understanding of the nature and structure of socio-psychological adaptability of personality provides new opportunities for considering this phenomenon as integral psychological construct. The purpose of the study is to develop scientific ideas about the correlation between various aspects of the adaptability of personality among young psychologists and tolerance to uncertainty in situations of interpersonal and professional interaction. The sample consisted of 60 graduate students of psychological and psychological-pedagogical university faculties studying under bachelor's degree programs. The diagnostic complex included tools for studying the cognitive, emotional and behavioral components of socio-psychological adaptability, as well as an individual's general and interpersonal tolerance / intolerance to uncertainty. The quantitative processing of the empirical data obtained using the Spearman correlation coefficient made it possible to detect significant correlation between various components of adaptability and manifestations of tolerance to uncertainty. The scientific novelty of the research lies in the formulation of the author's approach to the structure of the construct of socio-psychological adaptability of the individual and the possibilities of diagnostic and correctional work that open up in this regard. The conducted research has shown the need for the introduction of psychological and pedagogical support programs for students of helping professions in the process of studying at the university, aimed at the development of socio-psychological adaptability of the individual in connection with tolerance to uncertainty. In conditions of increasing social and economic instability, the study of these and other factors affecting the development of professional self-awareness will optimize the process of labor socialization of young professionals.
Forensic Investigation of Vehicle Identification Numbers and Documents at the Current Stage
Authors: Malykhina Ekaterina
Published: December 2023

Expansion of auto industry is inextricably linked to an ever growing number of vehicles. Obviously, under these circumstances it is crucial to enable utmost safety of auto market. One of the prioritized tasks of law enforcement agencies is to prevent vehicle-related crimes. Analysis of statistics indicates that vehicle theft is one of the most common offences. The purpose of this study is to look into modern ways and methods of forensic investigation of vehicle identification numbers and documents. The study focuses on auto parts and vehicle documents. This study, conducted on the basis of forensic and investigative practice and the conclusions of the forensic center of the Ministry of Internal Affairs of Russia, showed that the methods of forging vehicle markings and documents are constantly being improved and modernized, which requires law enforcement officers to perform high-quality work in order to prevent, disclose and investigate these illegal acts. The study revealed that ensuring efficient performance of expert departments requires a centralized database of vehicles under investigation and their documents, swift information exchange between various expert institutions, swift exchange of information in general, as well as a possibility to receive data on vehicles directly from their manufacturers.
CHARACTERISTICS OF MODERN YOUTH SOCIOELECTS IN THE FRENCH LANGUAGE
Authors:   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Motiash Hanna
Published: December 2023

Youth sociolect has recently become the subject of increased attention of academic linguists, however, this phenomenon has a long history. Youth sociolect is one of the spheres of emergence and the most active channel of the spread of language innovations: lexical, phraseological. The new in the vocabulary captures the dynamics of the present, the real state of the language, which cannot be ignored or bypassed only on the basis that it is not the norm. It manifests itself not only in the replenishment of the vocabulary of the language, in the appearance of new words and new meanings, but also in the change of the semantic structure of the word and its volume. Characteristic features of youth communication are a relaxed, informal, humorous tone of communication, a high degree of emotionality. This is ensured by advanced use of appropriate vocabulary. Numerous studies indicate that it is the youth sociolect that is distinguished by the active use of colloquial vocabulary and slang. Therefore, the study of the specific features of its lexical enrichment, the determination of the productivity of various means of replenishing its vocabulary, and the identification of the relationship between different ways of word formation are of great importance for the characteristics of the youth sociolect. The study of the youth sociolect is still relevant today.
Blended Learning Engagement Evaluation System for University in China
Authors: Yu Liu Et al.
Published: December 2023

This study uses the Delphi approach to create an evaluation system for blended learning, analyzing aspects affecting online learning. Educational institutions and decision-makers can use the system's three key dimensions and indicators to examine scientifically.Purposive sampling chose 17 Chinese university specialists for the study. With significant blended learning and learning engagement knowledge, the sample size met research objectives. Both face-to-face and telephone interviews provided deep insights into critical concerns. Experts reached a high level of agreement on evaluation indicators (? =.939,.849,.911, with average values above 4). The average Coefficient of Variation score was.167, ? =.851, showing high reliability. The review prompted indicator system changes. Experts disagree with the signs "Autonomous Time", "Habit", "Self encouragement", "Force", "Reward" and others, thus we should erase them. Additionally, "video learning time" and "learning Tool Use" average 3.82 and 3.71. Both standard deviations and coefficient of variation are below 1, indicating consistency. The indicators suggest substantial expert consensus, with standard deviations below 1. Each indicator has a coefficient of variation below.25, indicating strong standard alignment.
Exploring the Adoption of Blended Learning in Physical Education: A Study in Southwest China's Higher Education Institutions
Authors: Jing Fu Et al.
Published: December 2023

This study delves into the adoption of Blended Learning in Physical Education (BLPE) within higher education institutions in Southwest China. Employing structural equation modeling (SEM) on data from 654 respondents, which are higher education students major in physical education in southwest China. The research comprehensively examines both direct and indirect effects among the Information Quality (IQB), System Quality (SQB), Satisfaction (SAB), Perceived Ease of Use (PEB), Perceived Usefulness (PUB), Attitude Towards BLPE (ATB), Hedonic Motivation (HM), Price Value (PV), Habit (HT), Intention to Use BLPE (IUB), Facilitating Conditions (FC), Performance Expectance (PE), Effort Expectance (EE), and Social Influence (SI). Significant direct relationships were identified, including IQB's and SQB's positive effects on SAB (? = 0.358 and ? = 0.505, respectively), and SAB’s influence on PEB (? = 0.597) and PUB (? = 0.384). Additionally, mediation analysis revealed key indirect effects, such as SAB mediating the relationship between IQB and PEB (effect size = 0.206), and between SQB and PUB (effect size = 0.185). Notably, HM and PV did not significantly impact IUB, suggesting unique contextual differences in BLPE adoption in Southwest China. Other variables like HT, FC, PE, EE, and SI also showed significant impacts on IUB, reflecting the multifaceted nature of BLPE adoption. The study concludes that quality of content and system, along with user satisfaction, play pivotal roles in determining the success of BLPE. Contrasting with some global trends, cultural and contextual factors significantly influence BLPE adoption in Southwest China. These findings provide essential insights for educators and policymakers, emphasizing the need for contextually tailored BLPE strategies and highlighting the critical interplay of various factors in the successful implementation of blended learning in physical education.
Cultural Resources of Costume, Market Operation, and Cultural Capitalization Role of Cultural Heritage
Authors: Jinfang Ni Et al.
Published: December 2023

The conspicuous capitalization of costume culture within Chinese ethnic tourism villages is readily discernible. This paper aims to understand the impact of costume culture resources on costume culture capitalization, and to discusses the intermediary role of market operation and the moderate effect of cultural heritage. Using 402 survey valid data from the operators, managers, and practitioners of ethnic tourism villages three ethnic tourism villages (Nangang Millennium Yao Village, Zhaoxing Dong Village, Zhoucheng Bai Village) in China for empirical path analysis, mediation analysis and moderation analysis. The results indicates that clothing cultural resources positively impacts the capitalization of costume culture, and clothing cultural resources is the predictor of market operation, furthermore, the market operation plays a mediating role in the relationship between clothing cultural resources and clothing cultural capitalization. Significantly, cultural heritage positively moderates the impact of clothing cultural resources on the capitalization of costume culture. The research has expanded the internal mechanism of the influence of clothing cultural resources on the capitalization of costume culture, revealing the importance of cultural heritage in achieving the capitalization of costume culture in ethnic tourism villages.
A Context-Responsive LSTM based IoT Enabled E- Healthcare Monitoring System for Arrhythmia Detection
Authors: Pandiselvi T. , Dhevi B. Lakshmi , Karthik G.M. , T Vinoth , Shibu S. , Kumar C.Ramesh
Published: December 2023

Detecting Arrhythmia, a life-threatening cardiac condition, in real-time is crucial for timely intervention and improved healthcare outcomes. Traditional manual methods for Arrhythmia detection using Electrocardiogram (ECG) signals are error-prone and resource-intensive. To address these limitations, this paper presents an automated system based on the Context Responsive Long Short-Term Memory (CR-LSTM) model for real-time Arrhythmia classification. The system leverages IoT technology to continuously monitor vital signs and effectively combines contextual information with temporal sensor data to accurately discern different types of Arrhythmias. The CR-LSTM model achieves an impressive accuracy of 99.72% in multiclass classification of Arrhythmias, making it a promising solution for dynamic healthcare settings and proactive personalized care.
Intellectual Feature Ranking Model with Correlated Feature Set based Malware Detection in Cloud environment using Machine Learning
Authors: Rao Sanaboyina Madhusudhana , Jain Arpit , Gangadhar PVSS , Sowpati Vinay
Published: December 2023

Malware detection for cloud systems has been studied extensively, and many different approaches have been developed and implemented in an effort to stay ahead of this ever-evolving threat. Malware refers to any programme or defect that is designed to duplicate itself or cause damage to the system's hardware or software. These attacks are designed specifically to cause harm to operational systems, but they are invisible to the human eye. One of the most exciting developments in data storage and service delivery today is cloud computing. There are significant benefits to be gained over more conventional protection methods by making use of this fast evolving technology to protect computer-based systems from cyber-related threats. Assets to be secured may reside in any networked computing environment, including but not limited to Cyber Physical Systems (CPS), critical systems, fixed and portable computers, mobile devices, and the Internet of Things (IoT). Malicious software or malware refers to any programme that intentionally compromises a computer system in order to compromise its security, privacy, or availability. A cloud-based intelligent behavior analysis model for malware detection system using feature set is proposed to identify the ever-increasing malware attacks. The suggested system begins by collecting malware samples from several virtual machines, from which unique characteristics can be extracted easily. Then, the malicious and safe samples are separated using the features provided to the learning-based and rule-based detection agents. To generate a relevant feature set for accurate malware detection, this research proposes an Intellectual Feature Ranking Model with Correlated Feature Set (IFR-CFS) model using enhanced logistic regression model for accurate detection of malware in the cloud environment. The proposed model when compared to the traditional feature selection model, performs better in generation of feature set for accurate detection of malware.
Leveraging Machine Learning for Network Intrusion Detection in Social Internet Of Things (SIoT) Systems
Authors: S Divya , R Tanuja
Published: December 2023

This research investigates the application of machine learning models for network intrusion detection in the context of Social Internet of Things (SIoT) systems. We evaluate Convolutional Neural Network with Generative Adversarial Network (CNN+GAN), Generative Adversarial Network (GAN), and Logistic Regression models using the CIC IoT Dataset 2023. CNN+GAN emerges as a promising approach, exhibiting superior performance in accurately identifying diverse intrusion types. Our study emphasizes the significance of advanced machine learning techniques in enhancing SIoT security by effectively detecting anomalous behaviours within socially interconnected environments. The findings provide practical insights for selecting suitable intrusion detection methods and highlight the need for ongoing research to address evolving intrusion scenarios and vulnerabilities in SIoT ecosystems.
A Hybrid Probabilistic Privacy Preserving Based Community Detection Model on Online Social Networking Data
Authors: Shamila Et al.
Published: December 2023

Privacy preserving plays a vital role on the online social networking sites due to high dimensionality and data size. Community detection is used to find the social relationships among the node edges and links. However, most of the conventional models are difficult to process the community structure detection due to high computational time and memory. Also, these models require contextual weighted nodes information for privacy preserving process. In order to overcome these issues, an advanced probabilistic weighted based community detection and privacy preserving framework is developed on the large social networking data. In this model, a filter based probabilistic model is developed to remove the sparse values and to find the weighted community detection nodes and its profiles for privacy preserving process. Experimental results show that the filter based probabilistic community detection framework has better efficiency in terms of normalized mutual information, Q, rand index  and runtime (ms).
Enhancing Gastric Cancer Diagnosis through Deep Learning and Ensemble Techniques: A Comprehensive Analysis of Sub-database Performance
Authors: Angati Kalyan Kumar Et al.
Published: December 2023

Gastric cancer is the fifth most prevalent cancer worldwide and the fourth most fatal. Early detection is critical for effective treatment. Histopathological examination is the established diagnostic method for gastric cancer. Recent advancements in computer technology have accelerated the use of digital tools to aid pathologists in diagnosing gastric cancer from pathological images. Ensemble learning is employed to enhance algorithm precision, involving the integration of multiple complementary learning models.The experimental platform focused on three subdatabases within GasHisSDB. Four deep learning classifiers, specifically VGG19, Inception-V4, ResNeXt, and ResNet152, were employed for classification experiments on the GasHisSDB database. The evaluation encompassed performance metrics, including accuracy, precision, recall, specificity, and F1 score.In the examination of the 160 x 160 pixel sub-database, ResNet152 stood out by delivering exceptional results in both categories. It achieved a remarkable accuracy of 98.02% in the "Normal" category and 87.9% in the "Abnormal" category. In the 120 x 120 pixel sub-database, ResNeXt displayed strong performance with a 96.98% accuracy in the "Normal" category, while its accuracy dropped to 89.09% in the "Abnormal" category. Notably, in the 80 x 80 pixel sub-database, ResNet152 emerged as the top performer with a remarkable 98.67% accuracy in the "Normal" category and 95.12% in the "Abnormal" category. Across these diverse sub-databases, ResNet152 consistently outperformed other models, maintaining high accuracy and precision while ensuring balanced performance in both categories.
A Study of Challenges and Recommendations for Teaching: DevOps Education
Authors: R. Sravani, Vallem Ranadheer Reddy V. Shobha Rani, A. Ramesh Babu,
Published: December 2023

DevOps has gained significant popularity in the software industry due to its ability to enhance collaboration, improve software delivery, and drive organizational agility. However, providing effective education and training in DevOps poses several Issue. This research paper presents an interview study conducted with a seasoned DevOps professional to explore the challenges faced in DevOps education and provide recommendations for addressing these challenges. The study highlights the evolving nature of the DevOps landscape, the interdisciplinary nature of DevOps, and the need for hands-on learning experiences. Additionally, the study emphasizes the importance of continuous curriculum updates, practical exercises, cross-functional collaboration, industry partnerships, and fostering a continuous learning mindset. The findings from this study contribute to the understanding of the unique challenges and recommendations for improving DevOps education.
Design of Cognitive Rehabilitation Training System using Artificial Intelligence
Authors: Kuk-Se Kim Cheol-Seung Lee,
Published: December 2023

This study is a study to design a cognitive(dementia) rehabilitation training system using the MMSE-DS protocol and the GDS protocol using artificial intelligence to analyze the user's cognitive ability and infer cognitive domain content correlation inference algorithms. For research on cognitive judgment technology using artificial intelligence, We provide an integrated cognitive rehabilitation service platform, provide customized training content by building a cognitive rehabilitation evaluation and training user data storage and analysis database, and design an algorithm to help improve users' learning ability by building an artificial intelligence system.

The user's cognitive ability analysis and cognitive domain content inference algorithm using artificial intelligence is the purpose is to design a cognitive judgment platform and implement a system to apply cognitive evaluation to people with mild cognitive impairment and utilize cognitive rehabilitation content based on cognitive judgment technology system design technology. Through this study, we aim to provide direction for the future field of cognitive rehabilitation combined with artificial intelligence
Deep Learning for Fruit Grading: A State-of-the-Art Review
Authors: Jaydeep B. Patil Shobha B. Patil,
Published: December 2023

In the food industry, grading fruit quality is a critical responsibility. Throughout this process, fruits are sorted and categorized in by their quality. Fruit grading can be done using both machine learning and visual assessment. Visual inspection is subjective and can be influenced by human prejudice. Machine learning can produce more accurate and unbiased results. Deep learning-based methods can be used to evaluate the fruit quality by teaching a neural network to recognize various quality parameters like size, color, and defects. Deep learning methodologies for evaluating fruit quality offer further benefits. They are neutral and accurate, and they can manage enormous amounts of data. They can also save labor expenses and improve the efficiency of the grading process. Deep learning methods are useful for evaluating fruit quality, but they have several drawbacks. These include an intricate neural network, overfitting, and a lack of high-quality training data. Addressing these issues is crucial for the success of deep learning in fruit quality evaluation. In this paper, various significant deep-learning methods for evaluating fruit quality are described. The methods' advantages and disadvantages are also discussed. The study gives the researcher pointers on how to improve current strategies or create fresh ones to improve performance in terms of training effectiveness, accuracy, etc.
Boosting Attack Detection Capabilities in Multi-Tenant Distributed Systems via Meta-Ensemble Classifiers and Weighted Averaging
Authors: Maanav Chandwani, Aarushi Wagh Pravin Patil, Geetanjali Kale,
Published: December 2023

Multi-tenant distributed systems are a great way to share resources and scale performance, but they come with their share of security issues due to the introduction of more than one client on the cloud. Sharing of hardware and software resources among tenants gives rise to vulnerabilities and malicious tenants can misuse this shared data to launch attacks on other tenants. While efforts have traditionally focused on building secure network architectures, it is impossible to create a completely secure system due to its open-ended nature. This paper explores ways to detect malicious tenants on the cloud using machine learning algorithms. This paper proposes an ensemble-based meta-classifier to predict the probability of attack instantiation based on certain system parameter values. Additionally, this paper creates a dataset for analysis purposes and address the class imbalance problem often found in this domain where attack instances are rare. Satisfactory results were produced to distinguish between non attach and attack instances.
Hybrid Encryption based Access Control Approach for Securing Cloud Computing
Authors: M. Subhashini K. Shankar,
Published: December 2023

Cloud computing has become an integral part of modern technological infrastructure, facilitating the storage and processing of vast amounts of data. However, ensuring the security of sensitive information in the cloud remains a persistent challenge. This paper proposes a novel approach to enhance the security of cloud computing through hybrid encryption, leveraging the Whale Optimization Algorithm (WOA) and Cuckoo Search Optimization (CSO) algorithms. Hybrid encryption, combining symmetric and asymmetric cryptographic techniques, is employed to address the limitations of traditional encryption methods in cloud environments. The Whale Optimization Algorithm and Cuckoo Search Optimization are utilized to optimize key generation and management processes, enhancing the overall efficiency and security of the encryption scheme. The Whale Optimization Algorithm, inspired by the social behavior of humpback whales, is employed to optimize the parameters of the encryption algorithm. WOA's exploration and exploitation capabilities are leveraged to find an optimal balance in the encryption process, improving the overall robustness against potential attacks. Complementing WOA, the Cuckoo Search Optimization algorithm is applied to optimize the key distribution and update mechanisms. Modeled after the brood parasitism behavior of cuckoo birds, CSO excels in searching large solution spaces, making it suitable for refining the distribution of encryption keys and ensuring their constant adaptability to dynamic cloud environments.
Predicting Home Health Care Services Using A Novel Feature Selection Method
Authors: C. Arulananthan Et al.
Published: December 2023

In the hospital management and medical areas, the experience of the patients is seen as having a dominant reputation. Online patient reviews are acknowledged as a crucial factor in assessing the effectiveness and quality of healthcare services. Many people find the traditional method of assessing service excellence to be cumbersome. However, the data assessment and evaluation processes are now more informal and time-efficient thanks to machine learning classifiers and opinion mining tools. Patient satisfaction and hospital patient service quality currently play a significant role in the health care industry. This is achieved by foreseeing the various hidden patterns and pinpointing the essential elements that contribute to patient fulfilment. This work provides the novel feature selection method for identifying the key feature in Home Health Care Services using patient satisfaction data. Several components are examined to evaluate the superiority of different health care services based on diverse metrics. Various machine learning algorithms are applied to guess the significant aspects affecting patient healthcare satisfaction. As recognized, the patient experience is an indispensable for assessing the quality in healthcare services.
Evading and Averting the Sybil Attack in Manets Using Mac Hash Message Algorithm
Authors: M. Shivashankar Et al.
Published: December 2023

The security loopholes present in the wireless network especially MANET makes it vulnerable and weak. Most of the users are much concerned about the very security of the network and hesitate to participate actively in the transactions. Very powerful and strange attacks are pinpointed by many research scholars in the past. The Sybil attack is one of the most detrimental attacks imparted on MANETS where plethora of authentic nodes are faked and forged to enable illegal entry into a network to disrupt the very security of the MANET. The Sybil attack acts and simulates like an existing node present in the network to get unauthorized access into the network. To prevent and evade this a new algorithm using the MAC is employed in this paper. The proposed algorithm MAC Hash Message – MHM algorithm will detect, prevent and eliminate the Sybil attack completely and provides a hassle free transaction to the nodes present in the MANET.
An Ensemble Framework Approach to Crop Type Prediction Using Feature Selection and Multiclass Classification
Authors: A. Tamilmani Et al.
Published: December 2023

Crop type classification plays a crucial role in modern agriculture, aiding in yield prediction, resource management, and land-use planning. This paper presents a comprehensive framework for crop type classification utilizing a combination of feature selection techniques, robust classification Algorithm, and a Support Vector Machine (SVM)-based multiclass classification approach. The proposed framework begins with a novel feature selection process that identifies the most relevant attributes from the Agricultural Data and Rainfall data. This feature selection step is essential for reducing data dimensionality, enhancing classification accuracy, and improving model interpretability. Following feature selection, a state-of-the-art multiclass classification strategy based on Support Vector Machines is employed. SVMs are known for their capability to handle high-dimensional data and have demonstrated superior performance in various classification tasks. In this framework, SVMs are adapted to handle multiclass crop type classification efficiently. The model is trained on the selected features and optimized using hyperparameter tuning techniques to ensure robust performance.
The Proposed Framework for Cloud Computing System Security and Privacy Services
Authors: Neerav Nishant Et al.
Published: December 2023

Public cloud computing has emerged as a popular and efficient solution for organizations to store, process, and access their data and applications. However, with the increasing adoption of public cloud services, concerns regarding security have become a critical factor in decision-making processes. This study aims to investigate the security aspects of public cloud computing and identify potential vulnerabilities and threats that organizations may face. The aim of this study is to examine the challenges associated with cloud computing and its security concerns. Cloud computing is a technology that utilizes the internet to share data and resources. It is characterized by five key features: On-Demand self-service, Resource Pooling, Broad network access, Rapid elasticity, and Measured Service. However, despite these benefits, cloud computing has several open issues, including Security, Availability, Scalability, and Interoperability. Traditional security techniques are insufficient in fully securing cloud computing. Security and privacy issues often arise during cloud deployment and delivery models. As a result, researchers are interested in learning more about cloud computing security and its methods. Industries are creating architectural models with a high level of protection for their data center to solve current security challenges. This article examines the security of cloud deployment strategies and analyzes cloud security and privacy challenges. The cloud computing service models are also discussed, along with any security issues. The study emphasizes the significance of Audit as a key factor in offering a security solution that covers all relevant security characteristics as determined by the literature. The study's conclusions present a fresh challenge to cloud service providers and suggest a new security paradigm that explains customer security needs. Overall, this study offers a parameter-by-parameter assessment of available security solutions and makes recommendations for enhancements to address cloud security issues.
Hyperspectral Image Compression Using Prediction-based Band Reordering Technique
Authors: S Nithya Et al.
Published: December 2023

The hyperspectral image represents various spectral properties Because it consists of broad spectral information of ground materials that can be used for various applications, These images are collected as large amounts of data that must be processed and transmitted to the ground station. These acquired images contain redundant spectral information that has to be reduced in order to reduce transmission and storage capacity. This work focuses on preserving their quality while compressing them using band reordering techniques and prediction coding. This can be accomplished by preprocessing in which sub-bands are decomposed and bands are reordered into unsequenced compression can be accomplished through using the technique of linear prediction. The report discusses the Pavia University hyperspectral image data cube, which was acquired via a sensor known as a reflected optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy.
Deep Logo Authenticity: Leveraging R-CNN for Counterfeit Logo Detection in E-commerce
Authors: Preeti C M Et al.
Published: December 2023

In the rapidly evolving realm of electronic commerce, ensuring the accuracy and authenticity of merchandise assumes paramount importance in maintaining consumer trust and platform reliability. One of the prominent challenges encountered within this particular domain revolves around the pervasive prevalence of counterfeit products, often discernible through subtle deviations in brand insignias. This research paper introduces a novel approach to detect counterfeit logos on electronic commerce platforms using Region-based Convolutional Neural Networks (R-CNN). Traditional approaches often rely on manual verification or basic image comparisons, both of which have drawbacks in terms of scalability and consistent accuracy. The methodology utilized in our research capitalizes on the capabilities of deep learning algorithms to precisely identify and classify logos depicted in product images, proficiently distinguishing genuine logos from counterfeit ones with a significant degree of precision. A meticulously curated dataset was compiled, encompassing genuine and counterfeit logos sourced from renowned brands. By means of intensive training, our model demonstrated remarkable aptitude, surpassing the capabilities of contemporary methodologies. The current investigation not only offers a significant contribution to enhancing the security and reliability of electronic commerce platforms, but also establishes the foundation for the advancement of advanced counterfeit detection methodologies within the domain of digital marketplaces.
Enhancement of Real-Time Object Detection and Tracking in Collaborative Environment using AI and Mixed Reality
Authors: Anurag Tiwari Et al.
Published: December 2023

The area of mixed reality has had rapid growth in recent years, with a notable rise in funding. This may be attributed to the rising recognition of the potential advantages associated with the integration of virtual information into the physical environment. The majority of contemporary mixed reality apps that rely on markers use algorithms for local feature identification and tracking. This study aims to enhance the accuracy of object recognition in complicated environment and enable real-time classification operations via the introduction of a unique detection approach known as the lightweight and efficient YOLOv4 model. In the present setting, Computational vision emerges as a very valuable and engaging manifestation of artificial intelligence (AI) that finds widespread application in many aspects of daily existence. The field of computer vision is dedicated to the development of advanced artificial intelligence and computer systems that aim to replace complex elements of the human environment. In recent times, deep neural networks have emerged as a crucial component in several sectors owing to their well-established capacity to process visual input. This study presents a methodology for classifying and identifying objects using the YOLOv4 object detection algorithm. Convolutional neural networks (CNNs) have shown exceptional efficacy in the tasks of object tracking and feature extraction from pictures. Therefore, the enhanced network architecture optimizes both the precision of identification and the speed at which it operates. This research will contribute to developing mixed-reality simulations system for object detection and tracking in collaborative environment that are accessible to everyone, including users in the architectural filed. The model was evaluated in comparison to other object detection approaches. Based on the empirical results, it was observed that the YOLOv4 model exhibited a mean average precision (mAP) of 0.988, surpassing the performance of both YOLOv3 and other object identification models.
Arsenic As a Therapy in Cancer: A Short Review
Authors: Sabarni Sarkar  , Pritha Pal
Published: December 2023

Acute myeloid leukemia (AML) is a cancer of the blood and bone marrow in which the bone marrow produces immature white blood cells, called myeloblasts. AML (acute myeloid leukemia) prevents these myeloblasts from developing into mature, healthy blood cells, as a result they accumulate and crowd out other healthy cells, such as red blood cells, white blood cells, and platelets. Sometimes acute myeloid leukemia(AML) can also spread to other organs, such as the brain, spinal cord, liver, or spleen. Without prompt treatment, AML can be life-threatening. There has been 2–3 cases per 100,000 people annually. Compared to women, men are more likely to experience it. acute myeloid leukemia(AML) makes up roughly 3% of all malignancies, with a median age of 32 years, according to the population-based Cancer Registry for Delhi. Among the various drugs for treatment of leukemia arsenic has been one of the most effective in treating it. Arsenic has been known as poison over 4000years.Long-term exposure to as has led to various adverse effects on health, but after the discovery of chemo therapeutic properties of As the understanding has been changed from poison to drug. Arsenic trioxide (ATO) is the most active antileukemia agent in treatment of acute promyelocytic leukemia(APL). Arsenic trioxide (ATO) cause degradation of PML RAR alpha promoting differentiation. Not only this but also arsenic induces apoptosis via mitochondrial caspase or formation of reactive oxygen species (ROS) and deleting GSH content of cell.  This article focuses on the therapeutic effect of arsenic on acute myeloid leukemia
Probotics: A Promising Solution for Irritable Bowel Syndrome (Ibs)
Authors: Munnema Shabnom  , Ayan Datta  , Rupali Dhara Mitra
Published: December 2023

Irritable Bowel Syndrome (IBS) is a distressing functional gastrointestinal disorder that is increasingly recognized as a condition involving both the gut and the brain. In today's healthcare landscape, probiotics have become a common component of many people's wellness routines, independent of prescribed medications. There is substantial evidence supporting the potential of probiotics to provide therapeutic benefits for individuals dealing with irritable bowel syndrome. Recent studies emphasize the pivotal role of the microbial factor in the pathophysiology of IBS, as some research has identified significant alterations in the gut microbiome of individuals with IBS. Consequently, the impact of probiotics on IBS patients is under intense investigation in the current era of probiotic research. The precise mechanisms of action are not yet fully understood, but presently, probiotic products tailored for IBS relief are readily available in the market and have shown the potential to alleviate symptoms effectively. Evidence suggested that strains of Lactobacillus species, strains of Pediococcus species and Bifidibacterium species may show effective results in some patients of IBS. The aim of this study is to uncover potential causative factors contributing to IBS and to explore the use of probiotics as an efficacious treatment approach for IBS.
Al İÇERİĞİNİN (CoCrFe)60AlXNi(40-X) YÜKSEK ENTROPİLİ ALAŞIMININ YAPISAL VE MEKANİK ÖZELLİKLERİ ÜZERİNDEKİ ETKİSİ
Authors: POLAT Gökhan , ATALAY KALSEN Tuğba Selcen
Published: December 2023

Bu çalışmada, farklı alüminyum içeriğine sahip (CoCrFe)60AlxNi(40-x) alaşımları (x=5, 10, 20, 30 % at.) ark ergitme yöntemiyle üretilmiş ve 4 mm çapındaki silindirik bakır kalıp içerisine dökümü yapılmıştır. Elde edilen silindir şeklinde alaşımlar, XRD ve SEM ile yapısal olarak incelenmiş ve değişen oranlardaki Al ve Ni elementlerinin kristal yapıya, mikro yapıya ve mekanik özelliklere olan etkisi araştırılmıştır. Alaşım içerisinde oluşan fazlar, Thermo-Calc yazılımı kullanılarak ve termodinamik yaklaşımlar sergilenerek deneysel olarak elde edilen sonuçlarla kıyaslanmıştır. Alaşımların sahip olduğu mekanik özellikleri tespit etmek amacıyla sertlik ve basma testleri uygulanmıştır. Deneysel sonuçlar, alaşım içerisindeki Al miktarının %5 at.’den %30 at.’ye çıkmasıyla beraber, alaşımın temel kristal yapısının YMK yapıdan HMK’ye doğru geçiş yaptığını göstermiştir. Bununla beraber, alaşım içerisinde Al miktarının artması, mikro yapıda bir miktar AlNi intermetalik fazlarının da oluşmasına neden olmuştur. Al oranının artmasıyla hem kristal yapının değişmesi, hem de intermetalik fazların oluşması, alaşımın sertlik değerinin 146±3 HV’den 563±6 HV’ye kadar; akma dayancının ise 193 MPa’dan 1260 MPa değerine kadar yükselmesini sağlamıştır. Yapısal analizler ve mekanik testler, (CoCrFe)60Al20Ni20 YEA'sının mukavemet-süneklik dengesi açısından en umut verici alaşım olduğunu göstermiştir.
QUANTUM KEY DISTRIBUTION IN SMART HOME SYSTEMS
Authors: DERE Nurcihan , ÜLKÜ Eyüp Emre
Published: December 2023

In recent years, with the increase in the widespread use of the internet, the usage areas and the number of users have increased rapidly. Technological advances and efforts to make life easier bring innovation and transformation in many sectors. One of these innovative approaches is the use of Internet of Things (IoT) in home systems. The concept of IoT refers to the ability of objects to communicate with each other over the internet. Smart home systems stand out as systems where different devices come together work in interaction and can be controlled remotely. While this communication and integration offers various advantages, it also brings security risks. Ensuring security in IoT systems basically includes objectives such as confidentiality, integrity, and authentication. In this study, how the key used in the encryption of data transmitted between the gateway and the cloud in smart home systems can be strengthened with the B92 Quantum Key Distribution protocol is implemented through a scenario. The effect of the Quantum Key Distribution protocol in providing privacy and its advantages against man-in-the-middle attacks are emphasized in this study.
License Agreements used in Distribution of Machine Learning Software Packages
Authors: Kuzmenkov M. Yu.
Published: December 2023

Recently, consideration of the issues of legal regulation of artificial intelligence has become increasingly relevant. Most legal works are devoted to the study of responsibility of a legal personality that arises or does not arise from activities involving the use of artificial intelligence. However, for developers of the relevant software, the issue of intellectual property protection, namely, exclusive rights to computer programs, is more interesting. In particular, the types of licenses under which open source software products (such as MIT, GPL, BSD, Apach), their main characteristics, disadvantages and advantages are of interest. Of particular interest is the legal regulation of such license agreements in the context of cross-border turnover of software packages, as well as the existence of the open source concept. In addition, in order to dive deeper into the subject area of the study, the paper discusses basic concepts from the theory of machine learning.
Digital Ecosystem as a Way of Doing Business: Legal Perspective
Authors: Burova A. Yu.
Published: December 2023

The paper examines the digital ecosystem as a way of doing business, and highlights the elements that make up the digital ecosystem: the digital environment, subjects — participants in the ecosystem, products sold. The author analyzed Russian digital services that can be classified as digital ecosystems. It is established that it is possible to speak about business ecosystems, the functioning of which is aimed at making a profit, and ecosystems that do not have a commercial purpose. Based on the analysis, the elements of the ecosystem are determined, which include: the holding company and other companies that manage the digital ecosystem; digital platform operators; partners of digital platform operators who sell their products through digital platforms; ecosystem users who consume partner products and intermediary services of digital platform operators. The author concludes that the features of the digital ecosystem associated with its structure should determine the content of future legislative regulation of digital ecosystems, which is in its infancy.
Notarial Protection of Digital Rights of Corporate Participants
Authors: Tsvetkova E. S.
Published: December 2023

The paper is devoted to the study of methods for notarial protection of digital rights of corporate participants. The work examines the concept and types of digital rights, possible transactions that can be concluded at the external corporate level with digital assets, including cryptocurrencies and tokens, and assesses the legal regulation of the circulation of NFTs. The paper examines the risks of concluding smart contracts to which organizations are parties, and also evaluates their security and warranty, and examines the prospects for attracting a third-party arbitrator in the person of a notary in order to protect the rights and legitimate interests of the parties. The notary has long established itself as an effective mechanism for extrajudicial protection and protection of the rights of citizens and legal entities, and the presence of notaries’ own electronic infrastructure and a multi-level liability insurance system allows us to consider the notarial form of protection as an effective means of maintaining a balance of interests and the legality of concluded transactions. The issue of protecting digital rights is rapidly attracting more and more attention in civil science. The implementation of smart contracts on the Internet has revealed a number of problems, such as the impossibility of making changes to the concluded agreement and the influence of vice of will on the entire chain of relationships arising in blockchain technology. In terms of corporate participation in digital transactions, there are also many questions raised regarding verifying that the representative is properly authorized to enter into a transaction and that all procedures for approving the transaction and notifying interested parties have been followed. Since digital assets are classified as property, they are subject to civil law consequences related to this category of objects of civil rights.
The Problem of Recognition of Palestine in the context of the Palestinian People’s Realization of the Right to Self-Determination
Authors: Khasanov A. A.
Published: December 2023

The paper examines the problem of recognition of Palestine in modern international law. International documents and United Nations resolutions adopted on the issue of the conflict in Palestine are analyzed. The author, based on an analysis of international documents, concludes that Palestine meets all the criteria for statehood, but the Israeli occupation prevents the creation of a viable Palestinian state. Despite various efforts being made to reach a solution to the Palestinian issue, the peace process has reached a deadlock. This is facilitated by the policy and practice of the occupying power to consolidate the de facto position of the occupied territories in violation of its legal obligations. To date, Palestine is recognized by more than 138 states and the UN General Assembly has repeatedly confirmed the right to self-determination through resolutions. The efforts of the international community must be intensified to put an end to this irreconcilable conflict, which poses a threat to international peace and security.