International Journal of Communication Networks and Information Security (IJCNIS)
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    An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

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    With the rapid expansion of the Internet of Things (IoT), ensuring robust security and privacy for the vast network of interconnected devices has become paramount. Traditional security mechanisms often fall short in addressing the unique challenges posed by IoT environments, such as limited computational resources, energy constraints, and the need for scalability. This paper introduces the Efficient Lightweight Integrated Blockchain (ELIB) model, a novel framework designed to bolster IoT security and privacy while accounting for the inherent limitations of IoT devices. The ELIB model leverages the decentralized nature of blockchain technology to create a secure and transparent mechanism for IoT communications and transactions. By integrating lightweight cryptographic algorithms and optimized consensus protocols, ELIB significantly reduces the computational and energy demands typically associated with blockchain operations, making it suitable for resource-constrained IoT devices. Furthermore, the model incorporates privacy-preserving techniques to protect user data and ensure confidentiality in IoT networks. Through extensive simulations and real-world deployments, we evaluate the performance of the ELIB model in terms of security, privacy, scalability, and resource efficiency. The results demonstrate that ELIB outperforms existing IoT security solutions, providing enhanced security and privacy without compromising the performance or usability of IoT devices. Our model also facilitates seamless integration with existing IoT architectures, offering a practical and effective solution for securing the IoT ecosystem. This study contributes to the ongoing efforts to secure IoT environments, presenting a comprehensive model that addresses both security and privacy challenges. The ELIB model not only strengthens the security posture of IoT networks but also paves the way for the development of more resilient and trustworthy IoT applications and services.Internet of Things (IoT) is the most emerging tech- nology in the last decade since the number of smart devices, and its associated technologies are rapidly grown in both industrial and research prospective. The applications are developed using IoT techniques for real-time monitoring. Due to Low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography technique are not suitable. In this study, we initially reviewed and identified the security and privacy issue exists in IoT system. Secondly, as per Blockchain technology provides some security solutions. The details analysis, including enabling technology and integration of IoT technologies, are explained. Lastly, a case study is implemented using the Ethererum based Blockchain system in a smart IoT system and the results are discussed

    THE DEVELOPMENT OF AN EXPERT SYSTEM FOR THE IDENTIFICATION OF CAUSES, PREDICTIONS, AND REMEDIES IN REGARD TO AIRCRAFT DAMAGE

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    It is necessary to do routine maintenance, repairs, and upgrades on aircraft whenever possible in order to guarantee that operations run smoothly and to keep the pavements in a satisfactory state. It is possible to prevent aircraft breakdowns and ensure safety by performing periodic maintenance on an aircraft system and detecting failures or defects in the system at an early stage. Consequently, there is a requirement to incorporate cutting-edge technological systems into the process of aircraft repair. The purpose of this article is to contribute to the development of an expert system that can forecast failures, identify the factors that lead to failures, and offer solutions to aircraft failures. The probability tree was utilized in the research project to forecast faults in a selection of aircrafts, specifically the Boeing Aircraft (Year: 2016) and Airbus (Year: 2014) Model (BX2V3). These faults were then diagnosed by the expert system that was developed using the C++ programming language. The purpose of this system was to identify aircraft faults and offer solutions for a variety of faults that were identified

    Brain Tumor Segmentation and Classification Based on Deep Learning Using a Dense-Net Recurrent Neural Network

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    Brain tumors are malignant cellular growths that spread uncontrollably throughout the brain. The subsequent prognosis and treatment planning depend heavily on the accuracy of tumor segmentation. For the purpose of segregating tumors, Deep learning brings into consideration of unidentified location of malignancies inside certain regions when analyzing Magnetic Resonance Imaging (MRI) data. To achieve adaptable and efficient brain tumor segmentation, it first presents a pre-processing technique that focuses on a restricted region of the image rather than the complete image. This technique is simple and efficient since it analyses only a small portion of the brain image in each slice during the second phase, thus reducing computing time and avoiding the over fitting problem that plagued previous deep learning models. For feature extraction, a Recurrent Softmax Convolutional Neural Network (RS-CNN) based on the Dense Net Recurrent Neuron Network (DNRNN) is proposed. In addition, the Fuzzy Clustering Scaled Network (FCSN) mechanism is introduced to enhance the segmentation accuracy of brain tumors over existing models. To measure the performance, a Dense Net Recurrent Neural Network (DNRNN) is utilised to construct feature maps that modify the core network and classify the ensuing feature maps. These feature maps are then used to generate tumor area charts with prediction accuracy. The suggested method was evaluated on MRI brain images with malignancies using the Unique Client identification (UCI) data set. The results showed that the test time improvement enhanced the tumor segmentation accuracy

    Hybrid Machine Learning and Deep Learning Models for Efficient Detection of Arrhythmia from ECG Data

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    Arrhythmia, in its various forms, causes health issues worldwide. Traditional methods for diagnosing arrhythmia can help detect abnormalities in the heartbeat and provide necessary medical intervention. With the advancement of AI, it has become possible to analyze ECG data and detect different types of arrhythmia. Many researchers have contributed to developing ML and approaches for automatic arrhythmia detection. It has been observed from the literature that developing hybrid models using both ML and DL techniques can enhance performance in arrhythmia detection for developing a Clinical Decision Support System (CDSS). We suggest a hybrid technique in this study that combines ML and DL models, followed by a combination of deep learning models, to explore hybrid models in the arrhythmia detection process empirically. We introduce an algorithm called Hybrid Learning-based Efficient Arrhythmia Detection (HLEAD). Our empirical study with the benchmark dataset MIT-BIH revealed that the proposed hybrid models outperformed many existing arrhythmia detection models with the highest accuracy of 99.02%. Therefore, it is suggested that the proposed system developed based on hybrid DL and ML models could be integrated with healthcare applications to implement a CDSS for screening arrhythmias

    Enhancing Student Comprehension of Audio Mixing and Mastering Through Jigsaw Activity: A Study of Pedagogical Impact

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    Audio mixing and mastering are always being teach as a practice-based learning. The student-centered learning for this topic is related when student use their understanding and develop their skills in listening while adjusting knobs or audios technical which contributed to their final project. Jigsaw activity approaches is being selected to determine the student’s understanding towards audio mixing and mastering in practice-based learning. A quantitative and content analysis method is being conducted for this study. The method of jigsaw activity had been practiced in the class session which the result varied in four activities that being prepared to analyze the student’s understanding. The four activities are preparing slides, quiz, practical hands on, and questionnaire. The results shown the jigsaw contributed on enhancing student’s understanding towards mixing and mastering activities. Although the students applied their understanding more on the practical activities, the jigsaw activity still influence towards the student’s understanding in practice-based learning subject

    Intuitionistic Fuzzy Semihypergraphs

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    A Semihypergraph is a connected hypergraph in which each hyperedge must have atleast three vertices and each pair of hyperedges has atleast one vertex in common. In this article, the intuitionistic fuzzy semihypergraphs, semi µ- strong, semi ?- strong, strong IFSHGs and effective IFSHGs are introduced and some kinds of IFSHG such as simple, support simple, elementary, sectionally elementary IFSHGs are discussed

    Advanced Encryption Techniques in Biometric Payment Systems: A Big Data and AI Perspective

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    In the rapidly evolving landscape of biometric payment systems, the integration of advanced encryption techniques is crucial for ensuring robust security and privacy. This paper explores cutting-edge encryption methodologies tailored for biometric data in the context of big data and artificial intelligence (AI) applications. We investigate how these advanced techniques address the unique challenges posed by the vast amounts of sensitive biometric information generated and processed in modern payment systems. The study provides an overview of various encryption strategies, including homomorphic encryption, secure multi-party computation, and quantum-resistant algorithms, evaluating their effectiveness in safeguarding biometric data against emerging threats. Additionally, the paper examines the role of AI in enhancing encryption mechanisms and optimizing performance, highlighting how machine learning models can predict and mitigate potential vulnerabilities. By analyzing real-world case studies and empirical data, we offer insights into the practical implementation of these technologies and their impact on the security landscape of biometric payments. This research contributes to a deeper understanding of how advanced encryption and AI can collaboratively fortify biometric payment systems, ultimately paving the way for more secure and privacy-preserving financial transactions

    EFFICIENT BUS ROUTE DETECTION USING YOLOV5 AND IOT

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    Building on our prior research, currently under review for publication, which developed a robust system integrating YOLOv5 and PaddleOCR for efficient detection and recognition of bus routes, this chapter extends the system’s capabilities with innovative enhancements. We introduce a user-friendly interface that allows users to upload images or videos using a camera module sensor based on IoT from which the system can extract relevant details, a feature not present in the initial version. This enhancement significantly improves accessibility and usability, enabling seamless interaction with the system in real-world environments. Additionally, we integrate a spell check algorithm based on the Levenshtein distance to refine the textual outputs obtained from PaddleOCR, effectively correcting recognition errors and ensuring higher accuracy in identifying route numbers and destination names. Our enhanced system not only retains the high detection accuracy and efficiency of the original model but also significantly improves the overall user experience through better interaction and more reliable textual information. The results demonstrate that the incorporation of the UI and spell check algorithm markedly enhances the system’s practical application, making public transportation navigation even more accessible and user-friendly. This chapter contributes to the advancement of intelligent transportation systems, highlighting the importance of user-centric design and error correction in real-world applications, and provides valuable insights for researchers and practitioners in the field

    CONVOLUTION NEURAL NETWORK-BASED SPEECH EMOTION RECOGNITION USING MFCCS

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    Speech Emotion Recognition (SER) plays a crucial role in applications related to affective computing and human-computer interaction. Historically, many techniques for emotion recognition relied on simple feature extraction paired with basic classifiers. However, these traditional methods often demonstrated limited effectiveness in accurately identifying emotions. To address these shortcomings, this paper proposes five distinct models based on Convolutional Neural Networks (CNN) for recognizing emotions from speech signals. The methodology outlined in this approach focuses on recognizing seven emotions: disgust, neutrality, fear, joy, anger, sadness, and surprise. CNN is utilized alongside advanced feature extraction techniques, such as Pitch and Energy, Mel-Frequency Cepstral Coefficients (MFCC), and Mel Energy Spectrum Dynamic Coefficients (MEDC), to enhance recognition performance. These feature extraction methods have been shown to improve the classification of speech data, offering efficient processing times and improved voice quality, particularly through mel-cepstral coefficients. Once the features are extracted, they are fed into a CNN model. The proposed CNN architecture includes one or more pairs of convolutions and max-pooling layers, which process the input speech signals to classify the corresponding emotions. The model is implemented in MATLAB and evaluated against traditional methods like Linear Prediction Cepstral Coefficient (LPCC) combined with a K-Nearest Neighbor (KNN) classifier. For performance evaluation, various statistical measures such as accuracy, precision, specificity, recall, sensitivity, error rate, receiver operating characteristics (ROC) curve, area under curve (AUC), and False Positive Rate (FPR) are employed. These metrics help compare the effectiveness of the proposed CNN models against existing methods

    Blockchain Framework for Digital Learning and Information and Communications Technology

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    At present, the economic ties between countries worldwide are getting closer and closer. In a world where the internet industry is developing rapidly, Digital learning and ICT applications in blockchain have gradually matured. This paper takes digital learning and ICT blockchain application in e-commerce as the main research object, The rapid development of e-commerce has been promoted through the extensive application of digital learning and information and communication technology blockchain in e-commerce. Digital learning and information and communication technology solve the problems of e-commerce payment with encryption characteristics and security and openness in blockchain; At the same time, the information can be traced and cannot be tampered with to solve the quality problem of e-commerce goods. In a real sense to promote the sustainable development of the field of e-commerce, this study provides new ideas and guidance for the blockchain framework of e-learning and ICT in e-commerce

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    International Journal of Communication Networks and Information Security (IJCNIS)
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