bluuebunny/crossref_metadata_2025 huggingface dataset:
¿Qué es una vaca para un niño? Diez condicionantes pedagógico-artísticos de la expresión y representación infantil
Authors: Caeiro Rodríguez Martín , Fuentes Cid Sara , Cortizas Varela Olalla
Published: December 2023

La expresión artística en la infancia constituye una necesidad intrínseca que evoluciona en sincronía con el desarrollo cognitivo, perceptivo, comunicativo y psicomotriz de los niños y niñas. Aunque las etapas expresivas son comunes, las representaciones son singulares y se manifiestan como idiolectos, códigos particulares únicos en estilo, íntimamente conectados con la biografía de quien dibuja. Nuestra investigación se ha centrado en la interacción con niños y niñas de edades comprendidas entre los 2 y los 12 años, en su entorno familiar habitual, proponiéndoles el dibujo de una vaca. Las experiencias recopiladas han revelado una variedad de representaciones de la vaca, proporcionando una comprensión más profunda de los condicionantes presentes en la expresión y representación infantil. Además, hemos explorado la conexión entre estas manifestaciones artísticas y la adquisición de otros aprendizajes, como la lectoescritura alfabética. Este estudio destaca la importancia de que los maestros y maestras comprendan estos condicionantes pedagógicos expresivos. Al hacerlo, podrán responder de manera eficaz a las necesidades y posibilidades del universo expresivo infantil, enriqueciendo así el proceso educativo.
A kárpátaljai görögkatolikusok népességföldrajzi mutatói a Munkácsi Görögkatolikus Egyházmegye 2022-es sematizmusa alapján
Authors: Tóth Attila , Fodor Gyula , Berghauer Sándor
Published: December 2023

Ukrajna népességének vallási szerkezete mindig is számos kérdést vetett fel, nem kivétel ez alól Kárpátalja sem. A szovjet időszak kezdetétől hiányoznak pontos és használható adatok a felekezeti összetétellel kapcsolatban és a független Ukrajnában eddig első és egyetlen népszámlálás során sem volt lekérdezve a népesség vallásossága és vallási hovatartozása. Az adott homály eloszlására számos kutató készített különböző megítélésben részesült becslést.
Jelen tanulmányban arra tettünk kísérletet, hogy meghatározzuk a görögkatolikusok lélekszámát és részarányát a vidék népességén belül. A kutatás alapjául a Munkácsi Görögkatolikus Egyházmegye 2022-es sematizmusában közreadott adatok szolgáltak, amelyeket egy részletesen vázolt becslési módszer segítségével konvertáltuk használható formába. Továbbá bemutatásra került a Munkácsi Görögkatolikus Egyházmegye rövid történelme és a sematizmusokkal kapcsolatos legfontosabb információk.
Vizsgálataink eredményeire támaszkodva kijelenthetjük, hogy 2021-ben a megyében majdnem 240 ezer görögkatolikus élt. A területi elhelyezkedésüket tekintve, zömében a határmenti részeken és a megye három legnépesebb városa környékén összpontosulnak. Az össznépességen belüli arányát tekintve már egy kicsit más kép tárul elénk, ugyanis egyes hegyvidéki területek lakosságszámán belül jelentős a görögkatolikusok aránya, igaz az össznépesség száma is alacsonyabb.
A new approach to spatial localization of EEG-based electrical activity
Authors: Vartanov А. V.
Published: December 2023

Background. Accuracy and reliability of methods for spatial localization of brain activity based on electroencephalography (EEG) data do not lose relevance. Existing localization methods are coupled to specific difficulties due to ambiguity of the resulting solution.

Ojective: verification of a new method for localizing brain activity using EEG data «Virtually Implanted Electrode» and demonstratoin of its capabilities.

Material and methods. The new method was verified using deep brain stimulation (DBS) data. There were used the data from scalp electrical activity induced solely by potentials from two electrodes implanted into deep areas of human brain. It was achieved by filtering EEG signals at the stimulation frequency and excluding signals from other brain areas. A clinical case of applying the new method for EEG analysis of patient paroxysmal activity is presented as well.

Results. «Virtually Implanted Electrode» method allowed to quite accurately determine the localization of DBS current sources. The effectiveness of using the method for analyzing the mechanisms of paroxysmal activity was demonstrated: based on change in potentials calculated in 33 sites of the brain (brain areas), three groups of areas were identified specifically contributing to development of paroxysmal activity. For comparison, the same artificial DBS sources were localized using other verified methods: the BrainLoc (Russia) and sLORETA (Switzerland) software providing substantially worse data.

Conclusion. Identifying spatial localization of electrical potentials recorded on the scalp surface by using the new approach with the «Virtually Implanted Electrode» method shows high efficiency and reliability, demonstrating a clear advantage over other known localization methods.
Deep Reinforcement Learning Framework with Q Learning For Optimal Scheduling in Cloud Computing
Authors: Nihar Ranjan Sabat Et al.
Published: December 2023

Cloud computing is an emerging technology that is increasingly being appreciated for its diverse uses, encompassing data processing, The Internet of Things (IoT) and the storing of data. The continuous growth in the number of cloud users and the widespread use of IoT devices have resulted in a significant increase in the volume of data being generated by these users and the integration of IoT devices with cloud platforms. The process of managing data stored in the cloud has become more challenging to complete. There are numerous significant challenges that must be overcome in the process of migrating all data to cloud-hosted data centers. High bandwidth consumption, longer wait times, greater costs, and greater energy consumption are only some of the difficulties that must be overcome. Cloud computing, as a result, is able to allot resources in line with the specific actions made by users, which is a result of the conclusion that was mentioned earlier. This phenomenon can be attributed to the provision of a superior Quality of Service (QoS) to clients or users, with an optimal response time. Additionally, adherence to the established Service Level Agreement further contributes to this outcome. Due to this circumstance, it is of utmost need to effectively use the computational resources at hand, hence requiring the formulation of an optimal approach for task scheduling. The goal of this proposed study is to find ways to allocate and schedule cloud-based virtual machines (VMs) and tasks in such a way as to reduce completion times and associated costs. This study presents a new method of scheduling that makes use of Q-Learning to optimize the utilization of resources.The algorithm's primary goals include optimizing its objective function, building the ideal network, and utilizing experience replay techniques.
Deep Learning-Based Speech Emotion Recognition Using Librosa
Authors: D. Lakshmi Et al.
Published: December 2023

Speech Emotion Recognition is a challenge of computational paralinguistic and speech processing that tries to identify and classify the emotions expressed in spoken language. The objective is to infer from a speaker's speech patterns, such as prosody, pitch, and rhythm, their emotional state, such as happiness, rage, sadness, or frustration. In the modern world, one of the most crucial marketing tactics is emotion detection. For a person, you might tailor several things in order to best fit their interests. Due to this, we made the decision to work on a project where we could identify a person's emotions based just on their speech, allowing us to handle a variety of AI-related applications. Examples include the ability of call centers to play music during tense exchanges. Another example might be a smart automobile that slows down when someone is scared or furious. In Python, we processed and extracted features from the audio files using the Librosa module. A Python library for audio and music analysis is called Librosa. It offers the fundamental components required to develop systems for retrieving music-related information. Because of this, there is a lot of potential for this kind of application in the market that would help businesses and ensure customer safety.
Leveraging a Hybrid Deep Learning Architecture for Efficient Emotion Recognition in Audio Processing
Authors: Kirti Sharma Et al.
Published: December 2023

This paper presents a novel hybrid deep learning architecture for emotion recognition from speech signals, which has garnered significant interest in recent years due to its potential applications in various fields such as healthcare, psychology, and entertainment. The proposed architecture combines modified ResNet-34 and RoBERTa models to extract meaningful features from speech signals and classify them into different emotion categories. The model is evaluated on five standard emotion recognition datasets, including RAVDESS, EmoDB, SAVEE, CREMA-D, and TESS, and achieves state-of-the-art performance on all datasets. The experimental results show that the proposed hybrid architecture outperforms existing emotion recognition models, achieving high accuracy and F1 scores for emotion classification. The proposed architecture is promising for real-time emotion recognition applications and can be applied in various domains such as speech-based emotion recognition systems, human-computer interaction, and virtual assistants.
Study on Smart Designed Power Monitoring System Using IoT Devices
Authors: Noor Nateq Alfaisaly Et al.
Published: December 2023

This study has clearly proved that a well-designed power monitoring system is quite effective and necessary in the current year. During the same time, the integration of IOT devices with this system increased its effectiveness and use in a variety of disciplines. On the other hand, using this monitoring system makes it simple to maintain direct and effective connection with GSM and embedded controllers. Several sensors in smart design monitoring systems come in handy in this circumstance. Aside from that, the study's critical discussion revealed that smart power monitoring systems have a favourable influence on motion direction, home security, augmented reality, and technological disruption. The researcher gets data from 100 respondents out of a total population of 500. The sample for the study is chosen by the researcher using the basic random selection approach. The researcher prefers simple random sampling because it is the simplest way for selecting samples for the study. The researcher distributes the questionnaire to the study's respondents and so collects their responses in order to perform the research.
Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm
Authors: Nidhi Ranjan Et al.
Published: December 2023

Delivering scalable and on-demand computing resources to users through the usage of the cloud has become a common paradigm. The issues of effective resource utilisation and application performance optimisation, however, become more pressing as the demand for cloud services rises. In order to ensure efficient resource allocation and improve application performance, load balancing techniques are essential in dispersing incoming network traffic over several servers. The workload balancing in the context of cloud computing, particularly in the Infrastructure as a Service (IaaS) model, continues to be difficult. Due to available virtual machines and the limited resources, efficient job allocation is essential. To prevent prolonged execution delays or machine breakdowns, cloud service providers must maintain excellent performance and avoid overloading or underloading hosts. The importance of task scheduling in load balancing necessitates compliance with Service Level Agreement (SLA) standards established by cloud developers for consumers. The suggested technique takes into account Quality of Service (QoS) job parameters, VM priorities, and resource allocation in order to maximise resource utilisation and improve load balancing. The proposed load balancing method is in line with the results in the body of existing literature by resolving these problems and the current research gap. According to experimental findings, the Dynamic LBA algorithm currently in use is outperformed by an average resource utilisation of 78%. The suggested algorithm also exhibits excellent performance in terms of accelerated Makespan and decreased execution time.
ATiTHi: A Deep Learning Approach for Tourist Destination Classification using Hybrid Parametric Optimization
Authors: Tejaswini Bhosale Et al.
Published: December 2023

A picture is best way to explore the tourist destination by visual content. The content-based image classification of tourist destinations makes it possible to understand the tourism liking by providing a more satisfactory tour. It also provides an important reference for tourist destination marketing. To enhance the competitiveness of the tourism market in India, this research proposes an innovative tourist spot identification mechanism by identifying the content of significant numbers of tourist photos using convolutional neural network (CNN) approach. It overcomes the limitations of manual approaches by recognizing visual information in photos. In this study, six thousand photos from different tourist destinations of India were identified and categorized into six major categories to form a new dataset of Indian Trajectory. This research employed Transfer learning (TF) strategies which help to obtain a good performance measure with very small dataset for image classification.VGG-16, VGG-19, MobileNetV2, InceptionV3, ResNet-50 and AlexNet CNN model with pretrained weight from ImageNet dataset was used for initialization and then an adapted classifier was used to classify tourist destination images from the newly prepared dataset. Hybrid hyperparameter optimization employ to find out hyperparameter for proposed Atithi model which lead to more efficient model in classification. To analyse and compare the performance of the models, known performance indicators were selected. As compared to the AlexNet model (0.83), MobileNetV2(0.93), VGG-19(0.918), InceptionV3(0.89), ResNet-50(0.852) the VGG16 model has performed the best in terms of accuracy (0.95). These results show the effectiveness of the current model in tourist destination image classification.
Intelligent Traffic Monitoring System Using Vehicular Ad Hoc Network
Authors: Jayanthi Rao Et al.
Published: December 2023

The growing significance of road safety and human engagement in transport has emerged as a matter of national concern, exerting a profound impact on the lives of individuals.. Many road accidents and crashes failed to ensure human life safety. As a result, the traffic management system must maintain the balance in accordance with the maximum road limits. Vehicles with sensors and automated self-driving capabilities are now available, such as Tesla and others. The proposed system is based on a technique known as Intervention linear minimum spanning tree (ILMST), which employs a topology with lengths that are proportionally equal. When using dynamic topology, there is packet loss during a change of location or a continuous update in networking via vehicle movement from one location to another. In this manner, each node computes the weighted nodes with a number of partitions in order to provide a linear time update. This reduces the number of connected edges in the graph that are repeated. When the size of the repeated graphs that relate the GPS route from the maps is reduced, traffic updates avoid recursion and provide the best routes for customers. Traffic congestion overhead can be reduced by implementing the proposed methodology. It is possible to avoid it where there are traffic signals and all other sensor-based wireless devices in a vehicular Ad Hoc Network (VANET). The safety measures are also a necessary step based on the communications in routing and other protocols. The system, when combined with a neural network-based positioning system (NNPS) with various perceptrons, can maintain vehicle speed and categorize safety threats such as group classification. A solution can be found by repairing the DDoS attack based on the results of the various aspects that provide output for malicious monitoring.
DAR Model: A Novel Symmetric Key Enabled Security architecture for reliable data transfer in Wireless Sensor Networks
Authors: Seelamanthula Sreenivasu Et al.
Published: December 2023

Security is an indispensable aspect in every transaction happening in the network transmissions. Wireless Sensor Networks are pretty vulnerable to the security attacks. Hence a highly efficient architectural model is very much essential in designing the sensor networks. Cryptographic algorithms play a vital role in providing encryption and decryption to the data being transmitted consequently with which security is offered in an elegant manner. In this paper, a reliable design comprising three pioneering algorithms enabled with symmetric key is architected for secure communication in wireless sensor networks from a node to the base station. The design involves two phases. In the former phase two algorithms which are effective in all perspectives are used for data transmission from node to cluster head and in the latter phase another proficient algorithm is used for communication between cluster head to base station. The three algorithms used are Data Encryption Standard (DES), Advanced Encryption Standard (AES) and RC4. Both block and stream cipher algorithms are used to fine tune the performance; and in addition, the data has been compressed with unprecedented techniques to reduce the burden on encryption. This led to an amazing performance in terms of security parameters.
Cloud Storage Level Service Offering in Virtualized Load Balancer using AWS
Authors: Mohanaprakash T A Et al.
Published: December 2023

Distributed computing epitomizes an approach perfectly suited to the realm of IT commitments, leveraging the aggregation of information and resources through electronic cloud service providers utilizing interconnected hardware and software primarily based online, all at a reasonable cost. However, resource sharing can lead to challenges in their accessibility, potentially causing system crashes. To counter this, the technique of distributing network traffic across multiple servers, known as load balancing, plays a pivotal role. This paper ensures that no single server is overwhelmed, thereby preventing overloads and enhancing user responsiveness by equitably distributing tasks. Moreover, it significantly enhances the accessibility of tasks and websites to users. The fundamental objective of this concept is to comprehend load regulation, which operates in tandem with associated frameworks within communication structures like the Web. Load balancing stands as a critical domain within distributed computing, designed to prevent overburdening and to provide equally significant support. Various algorithms are employed to assess the system's complexity. In our proposed strategy, a process is outlined to determine optimal storage space utilization in real-time, utilizing 100 virtual computers, achieving an impressive 92% accuracy rate in its computations. This innovative approach promises efficient resource allocation within the distributed computing framework, thereby optimizing performance and accessibility for end-users.
Cyber security Reinforcement through Firewall Log Analysis and Machine Learning
Authors: Afrah Fathima Et al.
Published: December 2023

Firewalls play a crucial role as a primary protective measure in safeguarding network security, effectively mitigating risks posed by external vulnerabilities and internal security breaches. This study presents a new framework that utilizes firewall log data to classify incoming data packets as either permitted or forbidden. The dataset utilized in this research is obtained from Department of CS&IT,  MANU University and is subjected to a thorough data pre-processing procedure. This procedure includes several tasks such as managing missing values, encoding categorical variables, standardizing numerical attributes, and guaranteeing data coherence. In order to mitigate the issue of class imbalance within the target variable, we utilize a range of machine learning models and assess their efficacy through the examination of fundamental metrics such as accuracy, precision, recall, and F1-score. The results of our study demonstrate that the AdaBoost model has superior performance compared to other models, achieving a remarkable accuracy rate of 99.00%. This study demonstrates the application of machine learning methods to automatically identify the activities indicated in firewall logs, thereby improving the security of corporate networks. Through the implementation of automation, we facilitate a more dependable and efficient method of detecting and addressing possible risks, thereby strengthening network security measures and protecting valuable corporate information.
A Conceptual Cloud Forensic Investigation Process Model for Software as a Service(SaaS) Applications
Authors: Varshapriya N Jyotinagar Et al.
Published: December 2023

This paper explore a structured and systematic approach cloud forensic investigation process model for SaaS applications, to  investigate the digital crimes in the cloud environment and contributing to enhanced security and privacy of acquired data during forensic investigation .The proposed model offers the distinctive characteristics of cloud environments and the varying levels of access and control within them. In this proposed model, the systematic forensic investigation process is detailed with microscopic details  with four phases namely the initial phase, the acquisition phase, the analysis phase, and the reporting phase in Cloud environment. Ultimately, this research aims to enhance the overall trustworthiness and reliability of SaaS applications forensic for fostering a safer and more secure cloud computing forensic investigation landscape by using the chain of custody.
Audio Transcription and Summarization System using Cloud Computing and Artificial Intelligence
Authors: Kaushal Rajendra Khonde Et al.
Published: December 2023

In the modern era, organizations increasingly rely on virtual meetings to address customer issues promptly and effectively. However, dealing with recorded customer calls can be arduous. This review abstract introduces an innovative methodology to summarize audio data from customer interactions, which can streamline virtual meetings. Leveraging a speech recognizer, like AssemblyAI's API, the methodology converts audio data into text, and then employs a Graph-theoretic approach to generate concise summaries.

This review abstract delves into the growing prominence of cloud-based AI and ML services in the tech industry. It underscores the unique competitive strategies and focuses of major players, namely Amazon, Microsoft, and Google, in the realm of AI and ML platform development. The analysis explores these companies' internal applications and external ecosystem, dissecting their respective AI and ML development strategies. Finally, it predicts future directions for AI and ML platforms, including potential business models and emerging trends, while considering how Amazon, Microsoft, and Google align their platform development strategies with these future prospects.
BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis
Authors: D Muthusankar Et al.
Published: December 2023

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis.
Mental Depression Deduction Using Modified Regression Model to Prevent Suicidal Attempt
Authors: S. Poongothai Et al.
Published: December 2023

This study explores a novel approach for predicting depression using association-based multilevel linear regression. The suggested approach, known as association-based multilevel linear regression, uses data on mental depression to predict the prevalence of depression. Several statistical techniques can be used to forecast depression. Several statistical methods, including Linear Regression (LR), Multilevel Linear Regression (MLR), Naïve Bayes algorithm and Decision Tree (DT) were used in this investigation. Because these algorithms are able to predict mental depression based on certain characteristics such as precision and efficiency, their performance reduces. The results of these algorithms' predictions vary significantly, especially in terms of accuracy. The mental health data is fed into a developed model that has been trained to make predictions in order to address the aforementioned problem. Depression is the subject of conversation. A great degree of accuracy is shown by the association-based multilevel linear regression technique and the evaluation of prediction of accuracy in relation to other statistical methods. This study used association-based multilevel linear regression technique.  When compared to traditional methods, the method exhibits a substantially greater level of accuracy, almost 99%.
A Hybrid Classification Framework for Network Intrusion Detection with High Accuracy and Low Latency
Authors: Mahesh Kumar Et al.
Published: December 2023

Network intrusion detection (NIDS) is a crucial task aimed at safeguarding computer networks against malicious attacks. Traditional NIDS methods can be categorized as either misuse-based or anomaly-based, each having its unique set of limitations. Misuse-based approaches excel in identifying known attacks but fall short when dealing with new or unidentified attack patterns. On the other hand, anomaly-based methods are more adept at identifying novel attacks but tend to produce a substantial number of false positives. To enhance the overall performance of NIDS systems, hybrid classification techniques are employed, leveraging the strengths of both misuse-based and anomaly-based methods. In this research, we present a novel hybrid classification approach for NIDS that excels in both speed and accuracy. Our approach integrates a blend of machine learning algorithms, including decision trees, support vector machines, and deep neural networks. We conducted comprehensive evaluations of our approach using various network intrusion datasets, achieving state-of-the-art results in terms of accuracy and prediction speed.
Survey of Automatic Dysarthric Speech Recognition
Authors: Namita Kure Et al.
Published: December 2023

The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and human-machine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligence-based systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic Dysarthric Speech Recognition (DSR) using machine learning and deep learning paradigms. It focuses on the methodology, database, evaluation metrics and major findings from the study of previous approaches. From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR.
Imputation Techniques in Machine Learning – A Survey
Authors: Angeline Christobel Et al.
Published: December 2023

Machine learning plays a pivotal role in data analysis and information extraction. However, one common challenge encountered in this process is dealing with missing values. Missing data can find its way into datasets for a variety of reasons. It can result from errors during data collection and management, intentional omissions, or even human errors. It's important to note that most machine learning models are not designed to handle missing values directly. Consequently, it becomes essential to perform data imputation before feeding the data into a machine learning model. Multiple techniques are available for imputing missing values, and the choice of technique should be made judiciously, considering various parameters. An inappropriate choice can disrupt the overall distribution of data values and subsequently impact the model's performance. In this paper, various imputation methods, including Mean, Median, K-nearest neighbors (KNN)-based imputation, Linear Regression, Miss Forest, and MICE are examined.
The Idea of Digital Currency for India based on Blockchain Technology: A Conceptual Study
Authors: August Keshav Et al.
Published: December 2023

This paper aims to explain the idea of digital currency, its prospects in the Indian context, its role in digital transformation, and how it would be beneficial to our country, businesses, and different user segments. As in the phase of digital transformation, a constant improvement in technology is happening day by day globally. In today's virtual world, where everything is on its way forward, why should we not develop our economy accordingly so that we will be able to strengthen ourselves globally? And we had to begin the work of transformation by converting our currency to digital mode, i.e., digital currency, which is accessible in virtual or electronic form rather than physical.

The paper opted for an exploratory study using existing literature, case studies, books, electronic resources, speeches of various officials of the central bank, and experts on national as well as international platforms, which gave clarity on some of the core elements involved in digital currency, like the concept of blockchain with its kinds and segments like wholesale & retail and also with use case data in different counties across the globe, strengths, and weaknesses; initial coin offerings (ICO); and central bank digital currency (CBDC).

In this paper, data were complemented by documentary analysis, in which literature like the concept note of the Reserve Bank of India, the discussion paper of the Bank of England, and existing literature regarding centrally backed currency were studied for the clarity of the above work through figures like the money flower issued by the BIS (Bank for International Settlements). The paper concludes with discussion, suggestions, and future directions, with the conclusion that the user segments for the virtual currency backed by a central bank are classified.
KBD-Share: Key Aggregation, Blockchain, and Differential Privacy based Secured Data Sharing for Multi-User Cloud Computing
Authors: Badugu Ranjith Kumar Et al.
Published: December 2023

In today's era of widespread cloud computing and data sharing, the demand for secure and privacy-preserving techniques to facilitate multi-user data sharing is rapidly increasing. However, traditional approaches struggle to effectively address the twin objectives of ensuring privacy protection while preserving the utility of shared data. This predicament holds immense significance due to the pivotal role data sharing plays in diverse domains and applications. However, it also brings about significant privacy vulnerabilities. Consequently, innovative approaches are imperative to achieve a harmonious equilibrium between the utility of shared data and the protection of privacy in scenarios involving multiple users. This paper presents KBD-Share, an innovative framework that addresses the intricacies of ensuring data security and privacy in the context of sharing data among multiple users in cloud computing environments. By seamlessly integrating key aggregation, blockchain technology, and differential privacy techniques, KBD-Share offers an efficient and robust solution to protect sensitive data while facilitating seamless sharing and utilization. Extensive experimental evaluations convincingly establish the superiority of KBD-Share in aspects of data privacy preservation and utility, outperforming existing approaches. This approach achieves the highest R2 value of 0.9969 exhibiting best data utility, essential for multi-user data sharing in diverse cloud computing applications.
“Intrusion Detection System Evaluation “A Comparative Study of Machine Learning Algorithms
Authors: Praveen Kumar Shukla Et al.
Published: December 2023

The need for cheaper and faster delivery in the electronics industry has increased as a result of information technology advancements. The quick development of technology not only makes life simpler but also raises several security concerns. The number of attacks conducted online has increased as the Internet has developed through time. One of the supporting layers that can be used for information security is the intrusion detection system (IDS). IDS offers a clean atmosphere for conducting business and steers clear of shady network activity. The security on the user's end of web transactions is the most difficult task in the construction of an e-commerce system. This study examined intrusion detection security techniques. Continuous monitoring of intrusion detection is required for further technological adaptation, and as a result, presents a comparative comparison of adaptive artificial intelligence-based intrusion detection algorithms. This work shows how reinforcement learning (RL) and regression learning-based intrusion detection systems (IDS) can be used to solve extremely difficult issues, such as choosing input features and taking limited resources into account.
Simulating MIMT Attackers' Strategies in VANET to Secure ITS in Smart Cities via Multiverse Optimization-based Hybrid Routing Approach
Authors: Sumit Et al.
Published: December 2023

The connection between moving vehicles and stationary Road Side Units is made possible by VANET technology, which is an essential component of Intelligent Transportation Systems. Vanet based intelligent transportation system (ITS) security is major issue in present days. MIMT Attackers' Strategies are taken into account to test the security of VANET based ITS system in present research paper. The major objective of research paper is to evaluate the potential of security model in case of different type of message dropping and delay attack. It is observed that there is probability of message delay, message drop, and message tampering attack in VANET based ITS system. Due to such attacks there is huge impact on content delivery ratio, packet delay and dropping. Thus proposed hybrid routing approach that is considering Multi-verse optimization has been used to simulate the Quantifying MIMT attacks. In present research, Vanet security in case of intelligent transportation system in smart cities has been considered.
Thaat Classification Using Recurrent Neural Networks with Long Short-Term Memory and Support Vector Machine
Authors: Swati Shilaskar Et al.
Published: December 2023

This research paper introduces a groundbreaking method for music classification, emphasizing thaats rather than the conventional raga-centric approach. A comprehensive range of audio features, including amplitude envelope, RMSE, STFT, spectral centroid, MFCC, spectral bandwidth, and zero-crossing rate, is meticulously used to capture thaats' distinct characteristics in Indian classical music. Importantly, the study predicts emotional responses linked with the identified thaats. The dataset encompasses a diverse collection of musical compositions, each representing unique thaats. Three classifier models - RNN-LSTM, SVM, and HMM - undergo thorough training and testing to evaluate their classification performance. Initial findings showcase promising accuracies, with the RNN-LSTM model achieving 85% and SVM performing at 78%. These results highlight the effectiveness of this innovative approach in accurately categorizing music based on thaats and predicting associated emotional responses, providing a fresh perspective on music analysis in Indian classical music.
Machine Learning Techniques for Prediction of Climate Change in Nagpur using various Optimized Models
Authors: Bharati Masram Et al.
Published: December 2023

The Global temperatures are being affected by climate change. Nagpur's climate is a mix of wet and dry conditions. In the summer, the city experiences the driest weather. Because the city is approaching the extreme situation of climate change, it is vital to check the city's climate change parameters. In this study, we look at machine learning methods for predicting the climate, specifically the temperature factor for Nagpur. Three algorithms are discussed ,compare and contrast the Logistic Regression model, the Random Forest Classifier model, and the Auto Regressive approach model to find the optimum model for our prediction needs.
Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases
Authors: C Mohammed Gulzar Et al.
Published: December 2023

Introduction: Prediction and detection of skin ailments have generally been a hard and important task for health care specialists.  In the cutting-edge situation majority of the pores and skin care practitioners are the uses of traditional techniques to diagnose the ailment which may also take a large amount of time. Skin Diseases are excessive troubles in recent times as it is a consider form of environmental factors, socioeconomic elements, loss of entire weight loss program, and so on. Identifying the particular skin disease by computer vision is introduced as a novel task. Based on skin or pore disease, certain therapy can be suggested. In proposed study there are different applications based on deep learning are studied with computer vision task for better performance of proposed application. Famous deep learning algorithms may include CNN (convolutional neural network) , RNN (Recurrent Neural network), etc.

Objective: To diagnose skin disease with dermoscopic images automatically. Developing automated strategies to improve the accuracy of analysis for multiple psoriasis and skin diseases

Methods: In existing techniques many machine learning models are used which is having high complexity and require more time for analysis. So, in this study different deep learning models are studied for understanding performance difference between different models. This paper is a comparative check about skin illnesses related to ordinary skin issues in addition to cosmetology. Image selection, segmentation of skin disease detection and classification are the important steps can be used for oily, dry, and ordinary pores.

Result: The field of dermatology has seen promising results from studies on various Convolutional Neural Network (CNN) algorithms for classifying skin diseases based on clinical images. These studies have concentrated on utilizing the strength of deep learning and computer vision techniques to classify and diagnose different skin conditions using facial images precisely.

Conclusion: A survey of numerous papers is achieved on basis of technologies used, outcomes with accuracy, moral behavior, and number of illnesses diagnosed, datasets. Different existing research methodologies are compared with present deep learning architectures for understanding superior performance of deep learning models. Using deep learning, we can predict pore and skin diseases. In proposed study, introduction to different algorithms of deep learning which are combined with computer vision tasks to find the skin disease and pore disease are studied. Therapy can be predicted based on type of skin or pore disease.
Air Quality Prediction using Voronai-Based Spatial Temporal Sequence Similarity with Conjugate Gradient Enabled Sparse Autoencoder Deep Learning
Authors: P. Shree Nandhini Et al.
Published: December 2023

Air Quality Prediction (AQP) remains a difficult task because of multidimensional nonlinear spatiotemporal features. To solve this issue, an Improved Sparse Autoencoder with Deep Learning (ISAE-DL) and Enriched ISAE-DL (EISAE-DL) models were developed with the combination of concentric circle-based clustering, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) followed by the ISAE for AQP. In EISAE-DL, concentric circle-based clustering uses Manhattan distance to efficiently split the locations into four regions using its center and cluster the spatially and temporally similar candidate locations. But it was considered a fixed structure and may struggle to find variations in several data points. Also, it accommodate clusters with regular and circular patterns, whereas irregular and non-circular cluster patterns were not handled. Similarly, the ANN inference was often offended or ignored because of complex meteorological characteristics. Hence, this paper proposes a Voronoi-based spatial-temporal sequence similarity with the Conjugate gradient-enabled SAE-DL (VCSAE-DL) model for effective AQP. First, a Voronoi clustering is performed by creating the Voronoi diagram for analogous candidate location clustering. Then, the resultant clusters of location data along with the PM2.5 and other meteorological data are given to the Improved ANN (IANN), and the target stations are given to the LSTM to capture the spatiotemporal relationship features and temporal features, respectively. Also, CNN is used to extract relationships between terrain and air quality features. These extracted features are fused in the merge layer and transferred to the ISAE for final prediction of air quality. Finally, the test outcomes demonstrate that the VCSAE-DL achieves better prediction performance compared to the existing AQP models.
An Advanced High Dimensional Power System of Optimization Problems Algorithm
Authors: Hayder H. Safi Et al.
Published: December 2023

New Evolutionary Algorithm-based for High-Dimensional Power System Optimization Problems and Modern power system optimization problems face growing challenges as there are numerous decision variables and unbiased functions. Multi-objective evolutionary algorithms (MOEAs) basically are popularly applied for solving and dealing with such complicated problems in substantial power systems that have several objective functions. However, customary MOEAs works appropriately when the number of objective features comes to be less than three. Generally, the effectiveness of MOEAs begins to decrease apparently with an increase in the number of unbiased functions. This study is devoted to applying an authentic gradable evolutionary algorithm for the purpose of solving power system optimization issues which manifest numerous objective functions. In particular, this seeks an algorithm based upon NSGA-II algorithm dedicated for an efficient solution of multi-objective optimization problems. Here, we attempt to modify NSGA-II algorithm to effectively solve many target optimization problems. A novel effective grouping method is applied in the proposed algorithm in similar to the non-dominant grouping method of NSGA-II algorithm to accelerate decision convergence action in POF. The recommended algorithm is compared with modern numerous objective optimization algorithms following three test problems. The findings reveal that as the number of unbiased functions is noticeably large, the algorithm that we propose is significantly superior to other algorithms.
Blockchain Technology in the Intrusion Detection Domain
Authors: Issac K Varghese Et al.
Published: December 2023

The ability of blockchain technology to improve security and transparency across a range of industries has receivedA great deal of attention has been garnered lately in correcting the sentence.. In the domain of intrusion detection, where the identification and mitigation of cyber threats are paramount, blockchain has emerged as a promising solution. This abstract examines how blockchain is used in intrusion detection systems and emphasizes its advantages. Blockchain technology improves the security and integrity of intrusion detection systems by using a decentralized and immutable ledger. It provides an immutable audit trail, distributed consensus, and increased resilience to attacks. Moreover, blockchain fosters trust, transparency, and collaboration among stakeholders, enabling faster threat detection and response. This research can explore novel approaches to integrating blockchain into intrusion detection systems, providing stronger protection against cyber threats.Immutable Audit Trail: In the context of intrusion detection, the capacity of blockchain to produce an unalterable and transparent audit trail is of enormous value. Research in this area can focus on developing techniques to leverage the blockchain's audit trail for effective incident response, forensic investigations, and attribution of cyberattacks. We will use theweighted product model in this study, which is a research approach that gives weights to various factors and combines them to make conclusions based on their relative relevance in a weighted way. Taken as alternative is“IDS1, IDS2, IDS3, IDS4, IDS5, IDS6, IDS7, and IDS8”.Detection Quality, Performance, Stability, User Interface, Profile update, ConvenienceThe By this we can see that IDS4 has 1 RANK and IDS5 has the 8th RANK.In conclusion, blockchain technology holds great potential in the intrusion detection domain. Its decentralized and immutable nature can enhance the security and reliability of intrusion detection systems by providing transparent and tamper-proof logs of network activity. Blockchain-based solutions can improve threat detection, facilitate secure information sharing among entities, and enhance the overall resilience of intrusion detection systems. As the technology continues to evolve, further research and development in integrating blockchain with intrusion detection will unlock new possibilities for combating cyber threats.