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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shaji, Lijin Suji Pramila, R. Noorul Islam Center for Higher Education Thucklay Computer Applications Tamil Nadu 629180 India Mar Baselios Institute of Technology and Science Computer Science and Engineering Kerala Kothamangalam686693 India

ISBN: (纸本)9798350335095

Smart environments are worried about private keys and security problems. To prevent security attacks, which take benefit of a few of these intrusion detection systems (IDSs) and security vulnerabilities, were modeled. In the age of the Internet of Things (IoT), data generated by connected objects presents the fundamentals for big data analytics that could be used to recognize the anomalies and patterns in the data. Numerous cyber security systems use IDSs to detect intrusions that employ a variety of approaches and architectures. In this work, several research papers have been utilized based on diverse approaches used for detecting and preventing cyber security vulnerabilities. Moreover, this research work is reviewed by classifying them based on the detection approaches used in the preceding works. In addition, the research gaps and problems recognized in the previous works are stated;thus, the researchers can bring elucidation and improve their research in the future. The works in the literature are analyzed based on several features, such as performance evaluation measures, datasets utilized and approaches used. © 2023 IEEE.

关键词： Intrusion detection

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Video enabled crowd monitoring system and analysis 3

Video enabled crowd monitoring system and analysis

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mehra, Gunjan Joshi, Aditya Singh, Aakriti Shuaib, Mohd Diwakar, Manoj Singh, Prabhishek Graphic Era Deemed to be University Department of Computer Applications Dehradun India Graphic Era Deemed to be University Department of Cse Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798331515911

One of the most victorious strategies for slowing the spread of the COVID-19 virus has been proposed: social isolation. A computer vision-based AI-assisted approach for aiding with social distance norms compliance is utilized. The solution consists of modules for person detection, tracking, and distance violations detection. You are given the option to select a tool-based mode for camera calibration. In this paper, the various measures for determining the likelihood of social distance violations and how to tell them apart from temporary violations from persistent ones are demonstrated. On countless real-world issues, AI/Deep Learning has in the past shown promising results. In this paper, how to observe crowds in public areas and workplaces using Python, image processing, and computer vision is discussed in depth. © 2025 IEEE.

关键词： Deep learning

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Analysis and Evaluation of Bio-Inspired Algorithmic Framework, Potential Application in Cloud/Multi-Cloud Environment 5

Analysis and Evaluation of Bio-Inspired Algorithmic Framewor...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2023

作者： Kaur, Ramanpreet Anand, Divya Kaur, Upinder Verma, Sahil School of Computer Applications Lovely Professional University Phagwara India Baba Farid College Department of Computer Application Bathinda India School of Computer Science and Engineering Lovely Professional University Phagwara India Akal University Talwandi Sabo Department of Computer Science Bathinda India CGC Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350338287

Cloud computing allows web-based services to use a variety of reasonably priced services or resources, removing the requirements of centralized knowledge access. There are many provocations available in cloud computing and multi-cloud, so bio-inspired algorithms frequently used to handle particular problems such as load imbalance, resource equipping, and performance optimization. Bio-inspired algorithms have a proclivity for spontaneously resolving a wide range of challenges by giving optimum solutions. The ability has an impact on how bio-inspired algorithms are used to tackle the crucial issues in cloud computing. This study presents a comprehensive review of bio-inspired methods for resolving and optimizing solutions in multi-cloud. ACO, GA, PSO, FPA, BA and COSMIC bio inspired techniques are considered based on cost, scalability, fault tolerance, security, energy consumption, and throughput. ACO is secure and having capability of fault tolerance, PSO is fast but less secure and costly than others. Thus, FPA has a better capability than others for various parameters. FPA is better in security, cost, fault tolerance and energy consumption © 2023 IEEE.

关键词： Cloud computing

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Word Segmentation and Sandhi Resolution on Ayurveda Classical Scriptures 6

Word Segmentation and Sandhi Resolution on Ayurveda Classica...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Amrita Varshini, E.R. Nair, Jayashree Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350398496

Sanskrit is one of the oldest of the Indo-Aryan languages which has an abundant amount of texts in literature. One of the main sources of these Sanskrit texts is the textbooks written about the field of Ayurveda, the ancient Indian medical system. However, proper computational systems for processing this language are bare. A very challenging part of processing the Sanskrit language is the task of Sandhi resolution, which means the splitting of phonetically merged words. This study mainly focuses on this complex task of joint compound splitting and Sandhi resolution in Sanskrit specific to Ayurveda texts. This paper also surveys and compares all the existing research on Sandhi resolution in Sanskrit. In this work, an Ayurvedic dataset is compiled from some popular Ayurvedic texts, and a performance analysis of the state-of-the-art word segmentation model is carried out using this dataset. © 2023 IEEE.

关键词： Computational linguistics

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Pesticide Usage Forecasting for Agriculture using Prophet Model 5

Pesticide Usage Forecasting for Agriculture using Prophet Mo...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Wani, Afshan Hassan Moharana, Bhimasen Samant, Tapaswini De, Utpal Chandra Dey, Snehasis Banerjee, Shobhan Department of Computer Science and Engineering Lovely Professional University Punjab India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India College of Engineering Bhubaneswar India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350361155

Agriculture plays a vital role in our day-to-day life for the life sustenance of not just human beings but also various domestic and livestock animals. In addition to this, agriculture also contributes significantly to the economy of a country, impacting its growth across the globe. India had always been an agriculture-dominated country before the companies from the West started investing in India. But alongside agriculture come various challenges such as infestation by insects or weeds which eventually lead to the damage of crops and even diseases. But pesticides can even contribute to the contamination of soil, water, and other vegetation in areas where they are been used. In this paper, we have used the Prophet model to study the use of pesticides and analyze the trends of usage of organic pesticides over inorganic pesticides. © 2024 IEEE.

关键词： Pesticides

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Deep Convolutional Neural Network Approach for Tomato Leaf Disease Classification 3rd

Deep Convolutional Neural Network Approach for Tomato Leaf D...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data sciences, MIND 2021

作者： Lingwal, Surabhi Bhatia, Komal Kumar Singh, Manjeet Computer Engineering J C Bose University of Science and Technology YMCA Haryana Faridabad India Computer Applications J C Bose University of Science and Technology YMCA Haryana Faridabad India

ISBN: (纸本)9789811958670

Plant disease detection and classification are required to identify the early symptoms of disease in plants and crops to avoid their mitigation to entire croplands. Deep learning is rapidly getting involved in plant disease identification because of the large amount of image data which is otherwise difficult to learn. The paper has performed the tomato leaf disease classification based on convolutional neural network along with transfer learning where the data is collected from the PlantVillage dataset. The dataset comprises 10 classes containing nearly 16,000 leaf images. The data is divided into 70%, 20%, and 10% ratios into training, test, and validation set, respectively. The paper has shown a CNN model developed from scratch for learning, whose results have been compared with four different transfer learning models: DenseNet121, ResNet50, Inception-V3, and VGG-16. The models are evaluated using accuracy and cross-entropy loss. VGG-16 and CNN model developed from scratch has shown promising results on the given dataset with 90% and 83% validation accuracy, respectively, on the test set. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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AceRoute: Adaptive Compute-Efficient FPGA Routing with Pluggable Intra-Connection Bidirectional Exploration 24

AceRoute: Adaptive Compute-Efficient FPGA Routing with Plugg...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Wei, Xinming Zhang, Ziyun Zou, Sunan Sun, Kaiwen Zhang, Jiahao Zhang, Jiaxi Fan, Ping Luo, Guojie School of Computer Science Peking University Beijing China DeePoly Technology Inc. Beijing China Undergraduate School of Electronics Engineering and Computer Science Peking University Beijing China Center for Energy-efficient Computing and Applications Peking University Beijing China

ISBN: (纸本)9798400710773

This paper introduces AceRoute, an adaptive compute-efficient FPGA router that tackles the long-standing issue of lengthy FPGA compilation times given complicated FPGA architectures and designs to synthesize. We thoroughly profile modern FPGA routing patterns and identify the runtime hotspot: routing bottleneck connections in congested designs. However, previous works on routing acceleration hardly target mitigating connection-wise routing difficulties by characterizing device resource expansions and shifting path-exploration modes of connections. In this work, we propose a bidirectional intra-connection routing paradigm for the first time, which efficiently navigates congested device regions by initiating searches from both source and sink nodes. This approach significantly outperforms traditional unidirectional exploration in congested conditions. Furthermore, for each connection to route, we develop an adaptive strategy to select the optimal search mode online between uni- and bi-directional based on the current congestion situation. Our approach is pluggable and versatile, allowing seamless integration into existing FPGA routers and providing instant speed improvements with merely several hundred lines of code. Evaluation on FPGA24 contest benchmarks shows that our router, powered by adaptive bidirectional search, achieves over 2.4× and 3.2× faster routing on average with a serial and a 2-thread parallel version, respectively, compared with established routing tools RWRoute and Vivado. Additionally, when integrated into typical partition-based inter-connection parallel routers, our approach overcomes their inherent load-balance problems and amplifies the speedup to RWRoute from 2.2× to up to 5×. © 2024 Copyright is held by the owner/author(s).

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Traffic Sign Detection Using SSD Mobilenet & Faster RCNN 2

Traffic Sign Detection Using SSD Mobilenet & Faster RCNN

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Asif, Shaik Mohammad Anil, T. Tangudu, Satwik Keertan, G.H. Sai Don, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350347982

This paper presents a deep learning-based approach to traffic sign detection. The proposed approach utilizes state- of-the-art object detection models, such as SSD and RCNN, to detect traffic signs in real time. Tensorflow is used as a platform for training deep learning models and the models are being implemented on Google Colab and Kaggle cloud due to the GPU availability on the respective platforms. The models are evaluated on a traffic sign dataset from Kaggle, and it achieves high detection accuracy. Moreover, the proposed approach is robust to different lighting and weather conditions and is capable of detecting traffic signs from various distances and angles. The findings of this study show that by effectively detecting and recognizing traffic signs, deep learning-based techniques can potentially increase the safety and effectiveness of transportation networks. © 2023 IEEE.

关键词： Object detection

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Design of Fuzzy Logic Control Framework for QoS Routing in MANET

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Intelligent Automation & Soft Computing 2023年第3期35卷 3479-3499页

作者： M.Vargheese S.Vanithamani D.Stalin David Ganga Rama Koteswara Rao Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Master of Computer Applications M.Kumarasamy College of EngineeringKarur639113Tamil NaduIndia Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062Tamil NaduIndia Department of Computer Science and Information Technology Koneru Lakshmaiah Education FoundationVaddeswaram522502Andhra PradeshIndia

Wireless networks with no infrastructure arise as a result of multiple wireless devices working *** Mobile Ad hoc Network(MANET)is a system for connecting independently located Mobile Nodes(MNs)via wireless links.A MANET is self-conﬁguring in telecommunications,while MN produces non-infrastructure networks that are entirely *** the MAC and routing layers of MANETs take into account issues related to Quality of Service(QoS).When culling a line of optical discernment communication,MANET can be an effective and cost-saving route cull *** maintain QoS,however,more or fewer challenges must be *** paper proposes a Fuzzy Logic Control(FLC)methodology for specifying a probabilistic QoS guaranteed for *** framework uses network node mobility to establish the probabil-istic quality of *** Logic(FL)implementations were added to Network Simulator-3(NS-3)and used with the proposed FLC framework for *** have found that for a given node’s mobility,the path’s bandwidth decreases with time,hop count,and *** is resolutely based on this fuzzy rule that the priority index for a packet is ***,by avoiding sending pack-ets(PKT)out of source networks when there are no beneﬁcial routes,bandwidth is not *** FLC outperforms the scheduling methods with a wide range of *** improve QoS within MANETs,it is therefore recommended that FLC is used to synchronize ***,using these performance metrics,the QoS-responsible routing can opt for more stable *** on network simulation,it is evident that incorporating QoS into routing protocols is meant to improve traf-ﬁc performance,in particular authentic-time trafﬁc.

关键词： MANET quality of service fuzzy logic mobile node network trafﬁc

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Edge Computing Enabled Anomaly Detection in IoT Environments Using Federated Learning

Edge Computing Enabled Anomaly Detection in IoT Environments...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Husain, Saif O. Kumar, Rakesh Chinthamu, Narender Kumar, Manish Aswini, S. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Mit Cto DallasTX United States Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research explores the integration of edge computing and unified learning procedures for peculiarity locations in Internet of Things (IoT) situations. Four inconsistency discovery calculations - Isolation Forest, Local Outlier Factor, One-Class SVM, and Recurrent Neural Organize - are assessed over assorted IoT datasets to survey their adequacy. Results show that the Repetitive Neural Network outperforms other calculations with exactness, review, F1-score, and AUC-ROC values of 0.90, 0.92, 0.91, and 0.95 individually. The study illustrates the possibility of leveraging edge computing and unified learning for productive and privacy-preserving inconsistency locations at the organised edge. By passing on computational assignments to edge contraptions and collaboratively planning models utilizing combined learning, our approach finishes flexible and adaptable irregularity revelation in IoT systems. The investigate contributes encounters into tending to challenges such as inertness, security, and flexibility characteristics in centralized idiosyncrasy disclosure approaches, clearing the way for more capable and privacy-preserving courses of action in IoT applications. © 2024 IEEE.

关键词： Anomaly detection

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