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Annual IEEE Symposium on computer-Based Medical Systems

作者： Ayush Roy Anurag Bhattacharjee Diego Oliva Oscar Ramos-Soto Francisco J. Alvarez-Padilla Ram Sarkar Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Universidad de Guadalajara CUCEI Guadalajara Mexico Dept. of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15% and 79.79% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://***/AyushRoy2001/FA-Net

关键词： Solid modeling Pneumonia Fluids Computational modeling Lung Feature extraction Task analysis

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Enhancing Low-Power VLSI Design through AI-Based Simulation and Optimization

Enhancing Low-Power VLSI Design through AI-Based Simulation ...

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Smart Technologies, Communication and Robotics (STCR)

作者： Sheela Hundekari Onyekachi Kelvin Onumajuru Danish Multani Olutoye Timothy Oluwakemi Kamal Upreti Dept. of Computer Science Pimpri Chinchwad University Pune India Dept. of Computer Science University of Hertfordshire UK UST Global Solutions Gurgaon Haryana India Electrical and Electronics Engineering Olabisi Onabanjo University Ago- Iwoye Nigeria Dept. of Computer Science Christ University Delhi NCR India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

AI and ML techniques have dramatically influenced rapid developments in low-power VLSI design with fast advancements in device simulations and power optimization strategies. AI-based simulation tools are now used for accurate modeling of power consumption, improving thermal analysis, and quickening design iterations through the detection of inefficiencies and optimization of energy consumption. In fact, this work focuses on some AI-enabled methods of power reduction techniques such as voltage scaling, clock gating, and leakage current minimization with respect to a sustainable VLSI design. Moreover, a synthetic dataset is created to mimic the actual power consumption trend in VLSI circuits so that predictive modeling and regression techniques can be used for power estimation. Different regression models are used to check the predictive accuracy, and it was found that the highest R 2 score was 0.85 by Linear Regression, while the worst was achieved by Decision Tree Regression at 0.50. Results of the correlation analysis and models by machine learning clearly indicate that the frequency and operating voltage are the major contributors to consumption power, while gate counts have a relatively insignificant contribution. Introduction of AI in VLSI simulation enables the enhancement of power efficiency while maintaining sustainability outcomes by optimizing energy usage and cost reduction in terms of computation.

关键词： Analytical models Power demand Accuracy Computational modeling Very large scale integration Predictive models Data models Integrated circuit modeling Optimization Synthetic data

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection Systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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P-156: Efficiency Improvement Mechanism Analysis of Sidewall Passivation GaN based Micro-LEDs by Atomic Layer Deposition

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SID Symposium Digest of Technical Papers 2024年第1期55卷 1981-1983页

作者： Mengyuan Zhanghu Yibo Liu Zhaojun Liu Dept. of Electric and Electronic Engineering Southern University of Science and Technology Shenzhen China Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

Sidewall passivation using atomic layer deposition (ALD) can efficiently improve external quantum efficiency (EQE). Such treatment increases EQE, a combination of internal quantum efficiency (IQE) and light extraction efficiency (LEE), primarily by curtailing surface recombination according to the ABC model. Determined by the ratio of radiative recombination rate to total recombination rate, the IQE of an LED holds a significant correlation with the diode's ideality factor in the Shockley model. In this paper, we analysis the mechanism of ALD sidewall passivation enhancing device performance in detail from the aspects of electrical performance improvement and optical performance improvement.

关键词： Micro-LED EQE IQE LEE

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Performance Analysis of Hybrid MPI and OpenMP on Smith-Waterman Algorithm

Performance Analysis of Hybrid MPI and OpenMP on Smith-Water...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Keyur Ninama Jainamkumar Patel Girish K K Manideep Raya V S R S Reddy Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Maharshi Kanad QC Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In the rapidly advancing field of bioinformatics, sequence alignment is a pivotal task for elucidating genetic statistics and evolutionary relationships. As the volume and complexity of biological data continue to grow, it becomes imperative to employ effective computational techniques to manage this expansion. The Smith-Waterman algorithm is a key tool for sequence alignment; however, its performance can be constrained by the substantial size of contemporary datasets. To overcome this limitation, this paper explores a hybrid parallelization strategy that integrates message passing interface (MPI) with open multi-processing (OpenMP). This approach aims to significantly enhance the algorithm’s efficiency by leveraging the strengths of both parallelization models. By optimizing the scalability and execution speed of the Smith-Waterman algorithm on advanced high-performance computing (HPC) systems, the hybrid technique not only improves performance but also enables more rapid and accurate biological data analysis.

关键词： Data analysis Scalability Message passing High performance computing Memory management Parallel processing Genetics Performance analysis Bioinformatics Intelligent systems

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3D object detection with low resolution dept. map

3D object detection with low resolution depth map

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Wei-Ting Chen, Xiu-Zhi Chen, Yen-Lin Huang, Chieh-Sheng National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan Elan Microelectronics Corporation Artificial Intelligence Research and Development Department Hsinchu Taiwan

ISBN: (纸本)9798350324174

Obtaining high-resolution dept. map from radar and lidar for training dept. estimation network is a common approach in recent years, although the expensive price and high computational complexity cause it hard to be apply in real-world applications. In this research, we propose a novel concept that only requires low resolution dept. map for training, but able to reach similar performance comparing to high-resolution dept. map-based approaches. Refers to the experimental results, which evaluated on KITTI dataset, our proposed concept decreases 7% and 2% on the mAP of car and pedestrian comparing to the original approach in CaDDN, although it achieves similar performance on visualization result. © 2023 IEEE.

关键词： Object detection

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Off-Line Recognition System for Handwritten Arabic Words Using Artificial Intelligence 8

Off-Line Recognition System for Handwritten Arabic Words Usi...

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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Alaswad, Feisal Poovammal, E. Issa, Hazem Dept. of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Dept. of Computer Engineering Aleppo University Aleppo Syria

ISBN: (纸本)9781665435642

Arabic alphabets are used in more than 25 languages such as Arabic, Persian, Kurdish, Urdu etc. In this research work it is planned to build a computer system for recognizing handwritten Arabic words. We used sequence vector technique to recognize the Arabic words. Multi-layer Networks structure and Back-propagation Training are used as tools to decide. Also, for special cases of handling identicalness vectors, special feature extraction technique is applied. Experiments were performed by writing code in MATLAB, which achieved average accuracy of more than 92%. © 2021 IEEE

关键词： Backpropagation

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Addressing Data Imbalance in Machine Learning Solutions for Sexual Violence Detection

Addressing Data Imbalance in Machine Learning Solutions for ...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Azani Cempaka Sari Karto Iskandar Amrita Prasad Risma Yulistiani Aditya Kurniawan Nora Fitriawati Setiawan Joddy Rico Halim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Dept. of Mechanical Engineering Jadavpur University Kolkata India

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The case of sexual violence has become rampant especially with the introduction of the internet, social. Platforms act both as a means of perpetuating this violence through harassment of victims and as a platform for fighting back by sharing incidences of such occurrences. It is immediately crucial to obtain highly effective and accurate classification models for early identification and control of this behavior. This research aims to address data imbalance in detecting text-based sexual harassment in Indonesia using machine learning techniques by finding better ways of mitigating the imbalance that exists and where non-harassment cases dominate the dataset rather than harassment cases. This study is based on data extracted from Instagram, TikTok, and X (previously Twitter) with the Synthetic Minority Over-sampling Technique (SMOTE) to overcome the imbalance of data. Specifically, three machine learning algorithms were used: Support Vector Classifier (SVC), Random Forest Classifier, and Naïve Bayes Classifier were used. The SVC model had the highest accuracy of 97 percent under the accuracy, precision, recall, and F1-score categories. The findings clearly suggest that when applied with SMOTE as a preprocessing and SVC classifier, the problem of data imbalance is solved and harassment is detected more accurately. This paper further advances the area of Artificial Intelligence in online harassment, providing a novel approach to reach safer internet space and improve the assistance to the victims.

关键词： Accuracy Machine learning algorithms Video on demand Social networking (online) Static VAr compensators Vectors Safety Bayes methods Web sites Random forests

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FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-Rays

arXiv

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arXiv 2024年

作者： Roy, Ayush Bhattacharjee, Anurag Oliva, Diego Ramos-Soto, Oscar Alvarez-Padilla, Francisco J. Sarkar, Ram Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Universidad de Guadalajara CUCEI Guadalajara Mexico Dept. of Computer Science and Engineering Jadavpur University Kolkata India

关键词： Medical imaging

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis

PhishGuard: A Convolutional Neural Network-Based Model for D...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Md Robiul Islam Md Mahamodul Islam Mst. Suraiya Afrin Anika Antara Nujhat Tabassum Al Amin Dept. of Computer Science and Engineering Uttara University Dhaka Bangladesh Dept. of Informatics University of Oslo Oslo Norway Dept. of Building Engineering and Construction Management Khulna University of Engineering and Technology (KUET) Khulna Bangladesh Dept. of Electrical and Electronics Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science American International University Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Abstract-Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models’ decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a $1 D$ Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.

关键词： Uniform resource locators Deep learning Analytical models Accuracy Phishing Organizations Feature extraction

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