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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Selvarathi, C. Santhi, P. Deepa, K. Shri, G. Ramya Visali, C. Abirami, T. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamilnadu Chennai600 062 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirapalli India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Thalavapalyam639113 India Department of Information Technology Tiruchengode Namakkal637 205 India 638060 India

ISBN: (纸本)9798331508845

Creating groundbreaking solutions that benefit the populace is imperative in the modern era. The Internet of Things (IoT) serves as a trailblazing sector, fostering the development of contrivances automating an array of tasks. The IoT encompasses an interlinked network of cyber-connected contrivances executing functions with minimal human intervention. Capitalizing on IoT principles, this exploration endeavors to contrive a device aiding individuals grappling with mobility impediments. The envisioned gizmo consists of two primary sectors: a hardware module featuring a channel relay, Arduino, and an astute luminescent bulb, and a software element encompassing cloud and IoT platforms alongside a purpose-built app. The chief function of this contraption is to discern specific vocalizations in an individual's utterances, effectuating or deactivating the astute contraption to institute an intelligent habitation. In the initial phase, a luminous bulb will serve as a demonstration, employing for app origination. The distinctive aspect of this program lies in its vocalization-receptive microphone, with Bluetooth and Wi-Fi chips augmenting transmission velocity between the luminescent bulb and the mobile device. Meticulous attention will be accorded to auditory sensitivity and expeditious data transmission, with potential integration of diverse contrivances like ventilators and temperature-regulating apparatus into the envisioned gizmo. Furthermore, contemplation extends to a fortified safety attribute ensuring singular response to a particular vocal identity. © 2024 IEEE.

关键词： Luminescent devices

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Securing Smart Networks and Privacy Intrusion Detection System Utilizing Blockchain and Machine Learning 2

Securing Smart Networks and Privacy Intrusion Detection Syst...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Gnana Jeslin, J. Uma Maheswari, G. Amutha, S. Vargheese, M. Rajeshkumar, C. Valarmathi, S. RMK College of Engineering and Technology Department of Computer Science and Engineering Pudhuvayal Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India PSN College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Sri Krishna College of Technology Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365269

The digital landscape is facing threats from malicious actors, including malware, phishing, ransomware, and distributed denial-of-service attacks. This article introduces the Hybrid Intelligent Random Forest (HIRF) method, which combines machine learning and blockchain technology to detect anomalies in digital environments. HIRF has demonstrated exceptional accuracy rates in identifying and forecasting cyber-attacks, achieving 99.53% for the KD99 dataset and 99.53% for the UNBS-NB 15 dataset. It also minimizes false positives and negatives, enhancing network efficiency. HIRF's scalability and performance effectiveness make it suitable for government and business sectors, where it can enhance security measures and protect digital infrastructure against evolving cyber threats. Its potential applications extend to government and business sectors, where it can be instrumental in bolstering security measures and fortifying digital infrastructure against cyber threats. © 2024 IEEE.

关键词： Machine learning

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

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computers, Materials & Continua 2023年第10期77卷 1209-1226页

作者： P.Kalaiselvi S.Anusuya Department of Computer Science and Engineering Saveetha School of EngineeringChennaiTamil Nadu602117India Information Technology Saveetha School of EngineeringChennaiTamil Nadu602117India

In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so ***,researchers and industries have been highly endorsed in the medical field,predicting and controlling diverse diseases at specific *** tumor prediction is a vital chore in analyzing and treating liver *** paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks(CNN)and a depth-based variant search algorithm with advanced attention mechanisms(CNN-DS-AM).The proposed work aims to improve accuracy and robustness in diagnosing and treating liver *** anticipated model is assessed on a Computed Tomography(CT)scan dataset containing both benign and malignant liver *** proposed approach achieved high accuracy in predicting liver tumors,outperforming other state-of-the-art ***,advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver *** results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor *** can assist radiologists in their diagnosis and treatment *** proposed system achieved a high accuracy of 95.5%in predicting liver tumors,outperforming other state-of-the-art methods.

关键词： Deep learning convolution neural networks liver tumors CT scans attention mechanism classifier

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Histopathological Image Synthesis with Generative Adversarial Networks for Nuclei Segmentation

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SN computer science 2024年第1期5卷 123页

作者： Gour, Mahesh Rajpoot, Reenu Jain, Sweta Department of Computer Science and Engineering Maulana Azad National Institute of Technology MP Bhopal 462003 India

Generative adversarial network (GAN) has become a very popular and powerful tool in the field of medical images for generating synthetic images (mimic the images) of the original image. Synthesis of medical images is the newly emerging field of machine learning. The big challenge in the field of medical imaging is getting a large annotated image dataset for training deep learning models. More specifically, to build a deep model for nuclei segmentation from the histopathological images, very limited annotated data is available. To solve this problem, this study proposed a method for creating synthetical histopathological images using a GAN model. Generated synthetic images have been utilized as data augmentation to improve U-net model performance for nuclei segmentation. The proposed approach is tested on the publicly available multi-organ nuclei (MoNuSeg) dataset for nuclei segmentation from the histopathological images, and it achieves a mean intersection over the union (IOU) of 81.6%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Generative adversarial network (GAN) Histopathological images Nuclei segmentation Synthetical image U-Net

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Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms 3

Sentiment Polarity Analysis of Bangla Food Reviews Using Mac...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Amin, Al Sarkar, Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md Robiul Hoque, Md Mahmudul Uttara University Department of Computer Science and Engineering Dhaka Bangladesh National Institute of Technology Silchar India University of Oslo Department of Informatics Oslo Norway Maharishi International University Department of Computer Science Iowa United States CCN University of Science & Technology Department of Computer Science and Engineering Comilla Bangladesh

ISBN: (纸本)9798350388282

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024 IEEE.

关键词： Sentiment analysis

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Empowering Dynamic Scheduling in IoT Networks through Collaborative Policy Learning via Federated Reinforcement Techniques 2

Empowering Dynamic Scheduling in IoT Networks through Collab...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Thirunavukkarasu, M. Thaiyalnayaki, S. Jose, Naduvathezhath Nessariose Maheshwari, S. Pavithra, B. Malathi, G. Department of Computer Science & Applications S.A College of Arts & Science India Department of Computer Science Rabiammal Ahamed Maideen College for Women India Denken Solutions India Department of Information Technology Sri Sai Ranganathan Engineering College India Department of Computer science and Engineering Sri Sai Ranganathan Engineering College India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9798350383867

In the burgeoning landscape of Internet of Things (IoT) networks, efficient management of resources is paramount for ensuring optimal performance and resource utilization. Dynamic scheduling, particularly in the context of cloud-edge-terminal IoT networks, presents a significant challenge due to the diverse and dynamic nature of connected devices and their varying computational requirements. Traditional centralized approaches to scheduling may prove inadequate in such dynamic environments, necessitating the exploration of novel techniques. This project proposes a pioneering approach to address the dynamic scheduling challenges in IoT networks by leveraging collaborative policy learning through federated reinforcement techniques. The proposed framework harnesses the power of federated learning, a decentralized machine learning paradigm, to collectively train policies for dynamic scheduling tasks across distributed edge and terminal devices while preserving data privacy and security. Key components of the proposed framework include a collaborative learning architecture that orchestrates the exchange of policy updates among edge and terminal devices, enabling them to adaptively refine their scheduling policies based on local observations and feedback. Reinforcement learning serves as the underlying mechanism for policy optimization, allowing devices to learn and adapt to evolving network conditions and user demands over time. By decentralizing the learning process and leveraging the collective intelligence of edge and terminal devices, the proposed framework offers several advantages. These include improved scalability, reduced communication overhead, and enhanced resilience to network failures. Furthermore, the federated approach ensures data privacy and regulatory compliance by keeping sensitive information localized to individual devices. To evaluate the effectiveness of the proposed framework, comprehensive simulations and real-world experiments will be conducted u

关键词： Reinforcement learning

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An Integrated Representation & Compression Scheme Based on Convolutional Autoencoders with 4D-DCT Perceptual Encoding for High Dynamic Range Light Fields 8

An Integrated Representation & Compression Scheme Based on C...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Khaidem, Sally Sharma, Mansi Indian Institute of Technology Madras Department of Electrical Engineering Chennai India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798331531539

The emerging and existing light field displays are highly capable of realistic presentation of 3D scenes on auto-stereoscopic glasses-free platforms. However, the large size of light field data presents a significant drawback for 3D displays and streaming purposes. When a light field has a high dynamic range (HDR), its size increases drastically. This paper proposes a novel compression algorithm specifically designed for high dynamic range light fields that achieves perceptually lossless compression. The algorithm leverages inter-view and intra-view correlations within the HDR light field by treating it as a four-dimensional volume. This compression technique is based on the 4D-DCT Uniform Colour Space (4DDCT-UCS) algorithm. Further encoding of 4DDCT-UCS processed images using High-Efficiency Video Coding (HEVC) eliminates intra-frame, inter-frame, and intrinsic redundancies in HDR light field data. Our comparison with state-of-the-art coders like JPEG-XL and existing HDR video coding algorithms demonstrates the superior compression performance of the proposed scheme for real-world light fields. © 2024 IEEE.

关键词： Encoding (symbols)

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Advanced Encryption Standard (AES)-Based Text Encryption for Near Field Communication (NFC) Using Huffman Compression

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SN computer science 2024年第1期5卷 156页

作者： Ajagbe, Sunday Adeola Adeniji, Oluwashola David Olayiwola, Adedayo Amos Abiona, Seun Femi Department of Computer Engineering First Technical University Ibadan Ibadan 200255 Nigeria Computer Engineering Department Ladoke Akintola University of Technology LAUTECH Ogbomoso Nigeria Computer Science Department University of Ibadan Ibadan Nigeria

Data encryption which is associated with cryptography is necessary to prevent the compromise of Personally Identifying. Multi-level security is ensured by combining the Huffman code with certain cryptographic techniques, such as symmetric encryption algorithms. In order to decode the message, Huffman code can access both the code wordlist and the encoded bits. Inadequate error-correcting techniques with encoded bits can be harmed in this way, leading to the complete loss of information. Nevertheless, sending code wordlists and bits of code to the recipient takes a lot of transmission time. It is necessary to offer strategies for countermeasures to secure and fix the broken encoded bit. So, the problem is to safeguard the original wordlist from tampering without compromising the current code bit. Information. The goal of the investigation is to reduce interference in the levels of communication between the reader and the card. Due to the card's susceptibility, a Mifare classic 1K and Radio Frequency Identification (RFID) were used in this study's simulation. The experiment when N = 200 was conducted and the result below was obtained. The number of unique characters at 0.5 in AES running mode with 0.023 ms at CBC optimal and ECB 0.023 ms. It was possible to simulate the Advanced Encryption Standard (AES) using changing bytes and a number of unique characters. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Advanced Encryption Standard Applied informatics Cryptographic algorithms Huffman NFC RFID Security

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Time Complexity Reduction of CU Splitting in HEVC Intra Coding Using Zerocross Edge Detection Method 26

Time Complexity Reduction of CU Splitting in HEVC Intra Codi...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Islam, Md. Zahirul Redoy, Tanvir Ahmed Naznine, Mansura Khatun, Mastura Toufiq, Rizoan Marzouq, Mir Safwan Khaleque, Md Jawad Bin Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350359015

Video compression is a crucial technique for efficiently storing and transmitting videos over networks. The prevalent High Efficiency Video Coding (HEVC/H.265) standard achieves significant bitrate reduction but at the cost of increased time complexity. HEVC relies on coding tree unit (CTU) partitioning and Rate Distortion Optimization (RDO) during this process, which involves extensive rate-distortion trade-off searches. Determining the optimal partition for each Coding Unit (CU) within video frames poses a significant complexity challenge given the multitude of combinations to evaluate. As the number of CUs and partitioning grows, so does computational complexity. To address this, we propose an innovative heuristic model based on edge detection within HEVC's intra-mode. Our model employs the Zerocross edge detection algorithm identifying edges in each frame by detecting zero crossings in the second derivative of the image. Experiments show that up to 49.49% reduction in encoding time for intra-mode HEVC across 12 commonly used test video sequences is possible when implementing our approach and omitting the RDO process. However, this efficiency gain comes with an average 3.35% bit-rate increase compared to HEVC. © 2023 IEEE.

关键词： Coding Tree Unit (CTU) Partition Edge Detection Encoding Time Reduction High Efficiency Video Coding (HEVC) Rate Distortion Optimization (RDO)

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