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Video Surveillance-Based Intrusion Detection System in Edge Cloud Environment 3rd

Video Surveillance-Based Intrusion Detection System in Edge ...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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An AI-Based Chicken Disease Management System 1st

An AI-Based Chicken Disease Management System

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Quach, Luyl-Da Quoc, Nghi Pham Thi, Nhien Huynh Le Thanh, Nhan Thanh, Xuan Truong Thi Tran, Duc Chung Hassan, Mohd Fadzil Software Engineering Department FPT University Cantho city Viet Nam Computing Fundamental Department FPT University Hanoi Viet Nam Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia

ISBN: (纸本)9789811621826

Food stability has always received worldwide attention, especially in the development of the poultry industry. However, the poultry’s diseases have caused the loss of the poultry population and direct income of the owners. In this work, a system comprising of a website and a mobile application has been developed to support disease identification in chickens;the disease spread locations can be managed and traced with maps. In addition, the system allows farmers to make contact and get support from experts through chat, voice calls, and video calls. It also has some e-commerce functions for improving sales of poultry’s owners. The system uses the improved ResNet-50 model, with an accuracy of about 93.56%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Bridging the Rift: A Critical Perspective on the Divergence in Group Recommender Systems Research 25

Bridging the Rift: A Critical Perspective on the Divergence ...

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Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

作者： Ladislav Peska Amra Delic Francesco Barile Patrik Dokoupil Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic University of Sarajevo Sarajevo Bosnia Department of Advanced Computing Sciences Maastricht University Maastricht Netherlands

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm

Web Application to predict Skin Disease: An Approach Using D...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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Intrusion Detection in Smart IoT Devices for People with Disabilities 8

Intrusion Detection in Smart IoT Devices for People with Dis...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Naveed, Muhammad Usman, Syed Muhammad Satti, Muhammad Islam Aleshaiker, Sama Anwar, Aamir Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Department of Computer Science Islamabad Pakistan Ai Air University Faculty of Computing Department of Creative Technologies Islamabad Pakistan Riphah University Faculty of Computer Science and Software Engineering Islamabad Pakistan School of Computing & Engineering University of West London London United Kingdom

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

An intrusion Detection System (IDS) is a system that resides inside the network and monitors all incoming and outgoing traffic. It prevents unethical activities from happening over the network. With the use of IoT devices, network traffic is also increased. Intruders and hackers are attracted to this network because of its low processing power and openness. IoT has transformed diagnostic and monitoring systems for patients in the healthcare industry. However, a secure network is needed for these health care devices. This research proposes a hybrid model to secure the IoT network from external intrusions. The proposed method consists of preprocessing data with the help of normalization and feature selection by removing high correlated features with the help of the Pearson correlation coefficient and Support Vector Machine (SVM) for classification. The proposed approach has achieved an accuracy of 99.3%, precision of 99.1% and an F-1 score of 99.25% on the standard dataset. Results have been compared with state-of-the-art, and the proposed method outperforms all performance measures. © 2022 IEEE.

关键词： Support vector machines

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Assistant Solutions in software engineering: A Systematic Literature Review

Assistant Solutions in Software Engineering: A Systematic Li...

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IEEE International Conference on software engineering and Service Sciences (ICSESS)

作者： Alvaro Fernández Del Carpio Leonardo Bermón Angarita Department of Software Engineering Universidad La Salle Arequipa Perú Computing Department Universidad Nacional de Colombia Manizales-Caldas Colombia

Increasing technological advancements in developing assistants have gained attention because of its wide use in a variety of fields. In the realm of software engineering, developers are including these solutions in diverse software tasks. This research aims to identify and analyze the role and diversity of these solutions, including forms of interaction, functionalities, and technologies used for their implementation, evaluation, drawbacks, and current challenges. This research involves a systematic literature review of different approaches of assistant solutions in order to support software practitioners in developing their tasks. The solutions identified are oriented to recommendation systems, chatbots, and virtual assistants, covering a wide range of software processes such as requirements, design, construction, maintenance and, additionally management and process learning.

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Extracting Common and Variable Code using the LCS Algorithm for Migration to SPLE

Extracting Common and Variable Code using the LCS Algorithm ...

引用

IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Taeyoung Kim Jihyun Lee Sungwon Kang Department of Software Engineering Jeonbuk National University Jeonju Republic of Korea School of Computing KAIST Daejeon Republic of Korea

The LCS (Longest Common Subsequence) algorithm is a well-known algorithm that finds the longest subsequence from two different strings while preserving the relative order between the characters that make up the strings. When migrating from source code to software Product Line engineering (SPLE), the process of identifying commonality and variability is a crucial step and many studies have applied the LCS algorithm for this process, but this algorithm can be applied only to two sources although the cases of three or more sources are common. This study proposes a method that extracts common and variable code lines from three or more sources. The proposed method consists of the preprocessing phase that divides sources into sections by reflecting the characteristics of their programming languages, and the phase for applying the LCS algorithm. To evaluate the proposed method, we applied it to ArgoUML-SPL and compared the result with the original platform of ArgoUML-SPL, which confirmed that common and variable code lines were effectively identified.

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DeNC: Unleash Neural Codecs in Video Streaming with Diffusion Enhancement 39

DeNC: Unleash Neural Codecs in Video Streaming with Diffusio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Qihua Li, Ruibin Guo, Jingcai Huang, Yaodong Xu, Zhenda Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)157735897X

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-streaming restoration by decoder while lacking the proactive collaboration of encoder, (2) end-to-end optimization based on conventional rate-distortion theory, which has been verified that low distortion is not always a synonym for high perceptual quality, and (3) coupled design for domain-specific tasks that cannot generalize to various video codecs. Observing these limitations, our objective is not to incrementally present an improved restoration model. Instead, we focus on the encoder-decoder synergy, i.e., the codec, which is non-trivial since it inherently strikes the rate-distortion-perception trade-off of NeVS. Aiming at this target, we propose the Diffusion-enhanced Neural Codec (DeNC), a plug- and-play module for current NeVS paradigm, to significantly reduce the required bitrates while preserving high perceptual quality of restored videos. Our key design is twofold. First, DeNC improves the encoder’s compression efficiency by simultaneously reducing the resolution and color bit-depth of frame referencing. Second, DeNC empowers the decoder with perception-oriented restoration capability by making its diffusion-based restoration process aware of the encoder’s compression conditions. Real-world evaluations show that DeNC improves compression ratios with nearly an order of magnitude and achieves much higher restoration quality (e.g., 93+ VMAF and 23% higher MOS) over the latest baselines. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Video streaming

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An Analysis of Multicasting Optimisation Mechanisms for Intelligent Edge computing with Low-Power and Lossy Networks

An Analysis of Multicasting Optimisation Mechanisms for Inte...

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IEEE Conference on Wireless Communications and Networking

作者： Md Israfil Biswas Mohammed Al-Khalidi Muhammad Atif Ur Rehman Byung-Seo Kim Ali Kashif Bashir Department of Computing and Mathematics Manchester Metropolitan University Manchester UK Department of Software and Communications Engineering Hongik University Sejong City Korea

This work studies the built-in multicast model in Contiki OS to provide the basis of a comparative evaluation for a new optimisation model using Radio Duty Cycling (RDC) mechanism. A significant amount of energy is consumed at the edge node executing various multicast routing protocols in Low-Power and Lossy Networks (LLN). The optimisation of the routing protocol and selection of an efficient multicast transmission model has the potential to reduce energy consumption in Edge computing (EC) enabled LLN. With the precise objective of reducing energy consumption, this paper utilises a well-known RDC technique in multicast communication scenarios. To this end, a series of experiments are conducted to evaluate the performance of the existing RDC mechanisms proposed in the literature. The evaluation results are then utilised to develop an efficient RDC-based multicast transmission model. The comparative performance analysis reveals a 23.7% reduction in the RDC rate compared to the traditional model, consequently improving the energy consumption of EC-enabled LLN.

关键词： Energy consumption Analytical models Computational modeling Multicast communication Routing protocols Performance analysis Optimization

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