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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Kiruthiga, R. Nithya, B. National Institute of Technology Dept. of Computer Science and Engineering Tiruchirapalli India

ISBN: (纸本)9798350313796

With the huge increase in the amount of data that is transferred every second, congestion in the network becomes unavoidable. There is always a need for algorithms to control the congestion in the network when the data flow increases drastically. The traditional Transmission Control Protocol (TCP) uses a slow start and congestion avoidance mechanism that is not capable of differentiating the cause of packet loss. In a heterogeneous network, the packet loss could result either from congestion in the wired network or a transmission error in the wireless network. In this paper, we propose a machine learning based improvement over TCP-Jersey, a variant of TCP that can distinguish the cause of packet loss and handle it appropriately. TCP-Jersey contains two key components, the Available Bandwidth Estimation (ABE) algorithm and Congestion Warning (CW) router configuration. Our machine learning model learns to distinguish wireless packet loss from congestion packet loss using Multi-Layer Perceptron (MLP). It predicts the cause of the packet loss and handles the congestion using bandwidth estimation algorithm. The simulation results show that our algorithm outperforms TCP-Jersey, TCP-Reno, TCP-Tahoe and TCP-Vegas in terms of throughput, goodput, fairness index and friendliness index. © 2023 IEEE.

关键词： Packet loss

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Comparative Study of Convolutional Neural Network Object Detection Algorithms for Image Processing

Comparative Study of Convolutional Neural Network Object Det...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Reddy, Saieshan Pillay, Nelendran Singh, Navin University of Technology Dept. of Electronic & Computer Engineering Durban South Africa

ISBN: (纸本)9798350327816

This paper presents a comparative study on three Convolutional Neural Network (CNN) object detection algorithms to find the best detector based on the combination of speed and accuracy on a personal computer. The MATLAB® development environment is used to evaluate three different object detector algorithms, namely Faster Region-Based Convolutional Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). These algorithms are trained, and their performance metrics are tested on a small sample dataset. The results show that the SSD object detector algorithm performs best when considering both performance and processing speeds. Faster R-CNN detected objects at an average speed of 4.838 seconds and achieved a mean average precision of 0.76 with an average loss of 0.429. SSD detected objects at an average speed of 0.377 seconds and achieved a mean average precision of 0.92 with an average loss of 1.754. YOLO v3 detected objects at an average speed of 1.004 seconds and achieved a mean average precision of 0.81 with an average loss of 2.739. © 2023 IEEE.

关键词： computer Vision Convolutional Neural Network Image Processing MATLAB Object Detection

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Deciphering Deceit: Multimodal Classification of Optimal Algorithms for Fake Website Detection 3

Deciphering Deceit: Multimodal Classification of Optimal Alg...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Thombre, Supriya Doifode, Vaibhaw Kokardekar, Sakshi Hasija, Udit Wazalwar, Sampada Yeshwantrao Chavan College of Engineering Dept. of Computer Technology Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Electrical Engineering Nagpur India Kpit Technologies Limited Pune India Bhartiya Vidyapeeth College of Engineering Dept. of Computer Science and Engineering New Delhi India G.H. Raisoni College of Engineering Dept. of Information Technology Nagpur India

ISBN: (纸本)9798350354348

Detecting fake website URLs is crucial in today's digital era, where online scams and malicious/ fraudulent activities abound. Such websites pose a myriad of problems in the digital landscape, ranging from financial loss and identity theft to the dissemination of misinformation and the erosion of trust in online platforms. Individuals who fall victim to fraudulent websites may suffer financial repercussions, as they may unknowingly disclose confidential information such as details of credit card or login credentials. Moreover, fake websites can deceive users by spreading false information or promoting illegitimate products and services, which can undermine public trust in online sources and exacerbate societal issues such as misinformation and cybercrime. Therefore, there is a pressing need to detect fake websites promptly to mitigate these risks and protect users' privacy, financial security, and trust in the online environment. This study by the authors evaluates the predictive performance of five machine learning (ML) models - Random Forest (RF) Classifier, Decision Tree (DT)) Classifier, K-Neighbors (KN) Classifier, SGD Classifier and Gaussian NB (GNB) - in the reference of malicious and fake website URL detection. Leveraging a diverse dataset encompassing both benign and malicious URLs, our analysis encompasses rigorous evaluation metrics. The results highlight distinct strengths and weaknesses among the models, offering valuable insights for practitioners seeking to optimize predictive capabilities in the realm of cybersecurity. These findings contribute to the ongoing refinement of machine learning strategies for effective malicious URL detection and mitigation. © 2024 IEEE.

关键词： Phishing

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Ehsanu Hoque, Md. Habib Mahbub, Mrittika Bin Md Tomari, Mohd Razali Bashar, Rezaul Rashed, Md. Golam Das, Dipankar University of Rajshahi Dept. of Information and Communication Engineering Bangladesh Pundra University of Science and Technology Dept. of Computer Science and Engineering Bangladesh Dept. of Electronic Engineering Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (纸本)9798350391213

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification. © 2024 IEEE.

关键词： Traffic signs

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Leveraging ReRAM Sequence Graphs for Efficient Mapping of Binary Adders in ReRAM Crossbars 28

Leveraging ReRAM Sequence Graphs for Efficient Mapping of Bi...

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28th International Symposium on VLSI Design and Test, VDAT 2024

作者： Dhal, Pragnya Deb, Arighna Das, Subrata Das, Debesh Kumar Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Supreme Knowledge Foundation Group of Institutions Dept. of Computer Science & Engineering Hooghly India Jadavpur University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350380101

Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and memory in high-speed applications. However, existing methods for implementing addition of two n-bit operands within ReRAM/memristor crossbar structures do not ensure minimal device utilization or minimal ReRAM/memristor read-write cycles. In this paper, we propose a novel approach for implementing n-bit addition in ReRAM crossbars to optimize the usage of the ReRAM device and minimize read-write cycles. We develop the ReRAM Sequence Graph (ReSG) to efficiently represent adder functionality and systematically allocate ReRAM devices to ReSG nodes. Furthermore, we present a scheduling mechanism for implementing ReSG nodes within ReRAM crossbars, accelerating the execution of the addition operation. Experimental evaluations show that our method surpasses current approaches, achieving better device utilization and reduced read-write cycles. © 2024 IEEE.

关键词： Data transfer

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Automated Road Extraction in Urban and Rural Regions from Remote Sensing Imagery with U-Net and ResNet-Based Architectures 1

Automated Road Extraction in Urban and Rural Regions from Re...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350356816

Accurate road extraction from Remote Sensing Imagery (RSI) is crucial for various applications, including urban planning, navigation, and disaster response. This study investigates automated road extraction in rural, semi-urban and urban regions using deep learning architectures, specifically UNet and Deep Residual U-Net (ResNet) models. We leverage High-Resolution Satellite Imagery (HRSI) to train and validate our models, focusing on the distinct characteristics and challenges presented by urban and rural environments. Our methodology involves comprehensive data preprocessing and augmentation techniques to enhance model robustness. We evaluate the performance of both architectures using metrics and achieved an accuracy of 90.29%, recall of 80.95%, precision of 74.89%, and F1- score of 77.80% for the Deep Residual U-Net. Experimental results demonstrates that while both models perform well, the Deep Residual U-Net exhibits superior accuracy and generalization, particularly in complex urban landscapes. © 2024 IEEE.

关键词： Urban planning

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EMC and EMF Safety Aspects of Wireless Power Transfer Systems for e-Mobility

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IEEE Electromagnetic Compatibility Magazine 2024年第3期13卷 74-92页

作者： Maradei, Francesca Ahn, Seungyoung Campi, Tommaso Covic, Grant Cruciani, Silvano Hirata, Akimasa Wang, Shuo Feliziani, Mauro Sapienza University Dept. of Astronautic Electrical and Energy Engineering Rome Italy Daejeon Korea Republic of The University of Auckland Auckland New Zealand Tor Vergata University Dept. of Industrial Engineering Rome Italy Nagoya Institute of Technology Dept. of Electrical and Mechanical Engineering Nagoya Japan University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL United States University of L'Aquila Dept. of Industrial and Information Engineering L'Aquila Italy

This paper intends to provide a comprehensive over-view of the current state-of-the-art practices in wireless power transfer (WPT) technology within the automotive sector, while addressing pertinent issues related to electromagnetic compatibility (EMC) and electromagnetic field (EMF) safety compliance, as well as highlighting ongoing challenges and areas for further exploration. © 2012 IEEE.

关键词： Inductive power transmission

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Fuzzy Based Intelligent Transportation Systems for Smart Cities to Mitigate Road Traffic Congestion

Fuzzy Based Intelligent Transportation Systems for Smart Cit...

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2024 International Conference on Innovations in Science, engineering and technology, ICISET 2024

作者： Kabir, Md. Humayun Islam, Md. Saiful Hoque, Md. Jiabul Chittagong University of Engineering and Technology Dept. of Electronics and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350355499

Traffic congestion in smart cities is a growing concern. This paper explores the application of Fuzzy Logic in Intelligent Transportation Systems (ITS) to address this challenge. Fuzzy logic can handle imprecise and dynamic traffic data and offers advantages over traditional fixed-time signal control. The research proposes a Fuzzy-based ITS framework for dynamically traffic signal control. This framework utilizes real-time sensor traffic data to adjust signal timings, dynamically prioritizing congested areas. The system also incorporates emergency vehicle detection using siren sensors. This ensures priority for emergency vehicles by granting them the right of way. This paper discusses the design of the fuzzy inference system, including the selection of fuzzy variables and membership functions. The effectiveness of the proposed system is evaluated through simulations, demonstrating its potential to reduce traffic congestion and improve traffic flow in smart cities. © 2024 IEEE.

关键词： Traffic congestion

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Rana, Deepak Singh Dhondiyal, Shiv Ashish Singh, Sumeshwar Kukreti, Sanjeev Dhyani, Ashish Graphic Era Hill University Dept. Of Computer Science And Engineering Dehradun India Graphic Era Deemed To Be University Dept. Of Computer Science And Engineering Dehradun India Jaypee Institute Of Information Technology Dept. Of Computer Science And Engineering India Graphic Era Deemed To Be University Department Of Hospitality Management Uttrakahnd Dehradun India

ISBN: (纸本)9798350306446

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs. © 2024 IEEE.

关键词： Decision trees

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Near-Optimal Content Service Algorithm with Procurement and Placement in Edge Networks

Near-Optimal Content Service Algorithm with Procurement and ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yang, Chun-An Chen, Shih-Chieh Peng, Yi-Hsuan Chen, Yu-Wen Kuo, Jian-Jhih Tsai, Ming-Jer National Tsing Hua University Dept. of Computer Science Hsinchu Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan New York City College of Technology Computer Systems Technology BrooklynNY United States

ISBN: (纸本)9798350351255

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines. © 2024 IEEE.

关键词： Video streaming

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