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5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Chakraborty, Raktim Mandal, Jyotsna Kumar University of Kalyani Department of Computer Science and Engineering Kalyani India Raiganj University Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350347265

A 5nm MOSFET, CMOSFET and an Inverter circuit has been presented. The present work has deployed three different semiconductor agents and the analysis has been furnished in this paper. Silicon Germanium, Indium Gallium Arsenide and gallium nitride has been deployed and it has been found that silicon germanium is performing with optimal threshold voltage of 0. 1759V and providing maximum value of leakage current among of the three semiconductor candidates i.e. 601.1 x10-10A/um. Indium Gallium Arsenide is delivering maximum drive current 40.34 x10-7A/um among of the three other channel materials when a drain voltage is given of 0. 0005v. As an application of the 5nm Silicon Germanium based MOSFET, a CMOSFET and an Inverter circuit has been furnished. © 2023 IEEE.

关键词： III-V semiconductors

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Lightweight Convolutional Neural Network Model for Cassava Leaf Diseases Classification

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SN computer science 2024年第3期5卷 284页

作者： Tewari, Anand Shanker Computer Science and Engineering Department National Institute of Technology Patna Patna 800005 India

The world population has increased many folds in the last few years and crossed the figure of 7 billion. Africa has the highest population growth rate. Food and water are the first and foremost necessities for the survival of living beings. Cassava is among the staple foods of Africa and other countries. Its roots and leaves fulfill the daily caloric demands of millions of people. In the last few years, the production of cassava has decreased due to the spread of disease in cassava leaves. The manual identification of these diseases needs large number of people with sufficient level of skills in this field, so an automated technique is required that can help farmers by detecting cassava leaf diseases within a small time frame. The paper proposed a lightweight convolutional neural network (CNN) model to find four types of cassava leaf diseases i.e., cassava mosaic disease, cassava green mottle, cassava bacterial blight, and cassava brown streak leaf disease. It uses depthwise separable convolution operation to reduce the number of parameters that makes it suitable for mobile devices. The proposed model has used both channel attention and spatial attention to highlight disease part of the leaf and suppress background scenes. The modified attention mechanism of the proposed work helps in further improving the accuracy of the model. The experimental results exhibit that the proposed model outperforms other state-of-the-art models like VGG16, ResNet 50, EfficientNet, MobileNet V1, and MobileNetV2. The proposed model uses 1.1 million fewer parameters than MobileNet V2 that makes it suitable for low computing power devices. It uses the natural scene images for the classification that enhances its applicability in real-world scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Attention Convolutional neural network Deep learning Leaf disease classification

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A Bi-directional Gated Recurrent Unit and Long Short-Term Memory based Fake Profile Identification System 1

A Bi-directional Gated Recurrent Unit and Long Short-Term Me...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Geetha, B. Sushmitha, Boggarapu Ilanchezhian, P. Alabdeli, Haider Ahila, R. Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India Avn Institute of Engineering and Technology Hyderabad Department of Computer Science and Engineering India Sona College of Technology Salem Department of Information Technology India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, fake profiles are programmatically created online individualities that mislead and influence others, which are done by frequently using stolen photos, fraudulent information, or fictional particulars. Hence, these fake profiles are used for malicious activities such as phishing, social engineering, catfishing, and scattering misinformation. The expansion of fake profiles is increased with triggering more damage than any other cyber-attacks. The solution has been recommended in various ways to detect fake profiles, but the existing models have more overfitting problems. To overcome these issues, a combined model of Bi-directional Gated Recurrent Unit (BiGRU) and Long Short-Term Memory (LSTM) algorithms is proposed. Initially, data preprocessing is done on TwiBot-20 dataset with Natural Language Processing (NLP) and tokenization to remove unnecessary characters. Next, a word embedding technique name called GloVe is used to extract the optimal features, Finally, from the extracted features, the proposed BiGRU-LSTM model gives a precise rate in detecting and classifying fake profiles as human and bot generated. The proposed BiGRU-LSTM gives better results than the other compared existing methods Convolutional Neural Network (CNN) in terms of accuracy, precision, recall, and f1 score of 99.73 %, 98.23 %, 99.56 %, and 99.63 %respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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SynchroLearn: Master Deadlock and Synchronization Through Interactive Learning 15

SynchroLearn: Master Deadlock and Synchronization Through In...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bhavya, G. Bhanot, Shreya Srinidhi, M. Sidhu, Samreen Krishnan, Sajitha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Today, educational tools have become necessary for learners to learn well and improve their knowledge. These tools complement knowledge as they can make it more vivid and add some simulation or game into the learning process. It is self-learning since the students can learn from any place and it also enables the students to go further on content that may interest them, and which may help them in the understanding of the concept. This paper takes the idea of developing SynchroLearn, which is an educational visualization tool developed with the help of HTML, CSS, and JavaScript that enables the teaching of deadlock handling techniques effectively for both the developers and the students. To expound on techniques such as the Producer-Consumer problem, Peterson's method, Test-and-Set Lock, and the Dining Philosophers problem, an online tool known as SynchroLearn helps in providing the learner an actual time visualization. These strategies present several ways of handling resources, and with that, the management of their utilization process, so that the processes could be controlled in terms of what resources they require, which in turn, helps to solve and prevent such cases of deadlocks. This approach helps to learn complex relationships as well as to understand the interactions between the different processes, identify the deadlock situations, and use the essential methods for their resolution. They bring out the idea linking theoretical proficiencies and practical application by demonstrating them as simple-to-follow graphics which are therefore easier to implement. © 2024 IEEE.

关键词： Students

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Learning key steps to attack deep reinforcement learning agents

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Machine Learning 2023年第5期112卷 1499-1522页

作者： Yu, Chien-Min Chen, Ming-Hsin Lin, Hsuan-Tien Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Deep reinforcement learning agents are vulnerable to adversarial attacks. In particular, recent studies have shown that attacking a few key steps can effectively decrease the agent’s cumulative reward. However, all existing attacking methods define those key steps with human-designed heuristics, and it is not clear how more effective key steps can be identified. This paper introduces a novel reinforcement learning framework that learns key steps through interacting with the agent. The proposed framework does not require any human heuristics nor knowledge, and can be flexibly coupled with any white-box or black-box adversarial attack scenarios. Experiments on benchmark Atari games across different scenarios demonstrate that the proposed framework is superior to existing methods for identifying effective key steps. The results highlight the weakness of RL agents even under budgeted attacks. © 2023, The Author(s), under exclusive licence to Springer science+Business Media LLC, part of Springer Nature.

关键词： Reinforcement learning

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A Comparative Analysis of Supervised Machine Learning Models for Smart Intrusion Detection in IoT Network 3

A Comparative Analysis of Supervised Machine Learning Models...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Mittal, Saksham Mishra, Amit Kumar Tripathi, Vikas Singh, Prabhdeep Pandey, Priyank Department of Computer Science & Engineering Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350302288

IoT will become an integral part of everyone's life in making the world more intelligent and smarter. In an IoT ecosystem, objects collect and share information in order to communicate with one another. The objective of this paper is to design a safe and secure IoT network, which is more effective and smart in intrusion detection by identifying potential threats and different attacks and initiating an appropriate action. To build such a smart system, complex machine learning models are required which are trained on intrusion detection datasets. In this paper, at first two different benchmark datasets: CICIDS2017 and Bot-IoT are described briefly, which can be used for a smart intrusion detection system. After this, there is a discussion about the use of four different mathematical models using supervised ML algorithms on both the datasets respectively to classify different types of attacks, followed by a comparative analysis of the results obtained from the models in the results section. © 2023 IEEE.

关键词： Intrusion detection

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XGBoost-Liver:An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model

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computers, Materials & Continua 2025年第4期83卷 1459-1474页

作者： Sumaiya Noor Salman A.AlQahtani Salman Khan Business and Management Sciences Department Purdue UniversityWest LafayetteIN 47907USA New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11564Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200KPKPakistan

The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and *** diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic *** significant health risks and demand timely diagnosis and treatment to enhance survival ***,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive ***,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and *** study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver *** Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall *** performance of the proposed model has been extensively evaluated through k-fold cross-validation ***,the performance of the proposed model is evaluated using individual and hybrid ***,the XGBoost-Liver model performance is compared to that of commonly used classifier ***,its performance is compared with the existing state-of-the-art computational *** experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.

关键词： Machine learning deep neural network SHAP(SHapley Additive exPlanation) liver disease classifica-tion SMOTE(synthetic minority over-sampling technique)

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Improved bounding box regression loss for weapon detection systems using deep learning

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International Journal of Information Technology (Singapore) 2024年 1-17页

作者： Sumi, Lucy Dey, Shouvik Department of Computer Science and Engineering National Institute of Technology Nagaland Chumukedima 797103 India

Rising number of crime rate using firearms (such as open firing, robbery, suicides, mass shootings, homicides, threatening at gun point, etc.), has underscored the growing importance of timely detection of weapons. Bounding box regression, an essential element in object detection, plays a crucial role in accurately identifying and localizing these firearms. This paper introduces a Manhattan-Complete IoU (MCIoU) loss for bounding boxes, which demonstrates significantly faster convergence during training compared to other IoU losses. By incorporating MCIoU into one of the advanced object detection framework, (You Only Look Once) YOLOv7, the proposed work demonstrates consistent improvement on their performance across popular weapon detection benchmarks datasets such as Granda, Synthetic, Cam157, Internet Movie Firearms Database (IMFDB), Gun Movie and Monash. The most encouraging outcomes were obtained on a Gun movie dataset with a precision and recall value of 98.2% and 96.3% respectively, which is an appreciable improvement compared to the baseline YOLOv7 model. Experiments show that the best precision achieved was at 98.2% and mAP@.50:.95 of 42.5% over other existing IoUs. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CCTV surveillance Deep learning Intersection over union (IoU) Loss function Weapon detection system You Only Look Once v7 (YOLOv7)

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Automation of Text Summarization using Hugging Face NLP 5

Automation of Text Summarization using Hugging Face NLP

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Asmitha, M. Danda, Aashritha Bysani, Hemanth Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer science Universiti Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

Within the expansive domain of"Natural Language Processing" (NLP), the task of"text summarization" emerges as a foundational element, playing a pivotal role in distilling relevant information from extensive textual corpora. In the digital age, the importance of efficient summarization becomes increasingly critical, given the overwhelming volume of textual information. This comprehensive study delves into the intricacies of both extractive and abstractive summarization techniques, placing a specific focus on transformer-based models like BERT and GPT. These models, celebrated for their remarkable capabilities in context comprehension and coherent summarization, are rigorously evaluated alongside established methods like TF-IDF, TextRank, Sumy, Fine Tuning Transformers, Model-T5, LSTM, greedy, and beam search. The practical implications of text summarization extend across diverse fields, encompassing news stories, academic papers, and social media content, underscoring its broad utility in various domains. This study not only incorporates cutting-edge models but also explores a gamut of evaluation methods to discern the quality of summarization. By intertwining theory and application, this research positions itself at the forefront of evolving summarization approaches, shedding light on the transformative impact on information consumption patterns. The dynamic landscape of summarization methods underscores the need for continuous research and innovation, as technological advancements continue to reshape how individuals access and comprehend information. © 2024 IEEE.

关键词： Quality control

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A Comprehensive Von Willebrand Disease Awareness and Support Chatbot for Senegalese Communities 39

A Comprehensive Von Willebrand Disease Awareness and Support...

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39th International Conference on Information Networking, ICOIN 2025

作者： Akuthota, Keerthi Sai Reddy, C. Kishor Kumar Shuaib, Mohammed Alam, Shadab Alshanketi, Faisal Kyatham, Aashritha Reddy Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798331506940

A lack of the protein required for healthy blood coagulation results in Von Willebrand disease (VWD), a blood condition. The absence of readily available, thorough information to assist individuals with VWD in managing their illness is one of their biggest problems. Many patients believe they lack access to all the necessary resources, which is made worse by language and technology literacy challenges, especially in places like Senegal, where French and Wolof are the main languages. Senegal is a small coastal African country. Senegalese are made up of at least six ethnic groups, with the Wolof being the largest. With a particular focus on the Senegalese community, creating an educational intelligent chatbot for VWD sufferers seeks to address these issues by improving literacy and filling in knowledge gaps. Users can communicate with the system regardless of their technological expertise thanks to this chatbot's use of speech recognition and machine learning technologies to provide precise, culturally relevant, and understandable information in both French and Wolof. The chatbot is intended to significantly improve the quality of life for VWD patients, especially in Senegal, by providing dependable, current materials in a safe online setting, equipping them with the knowledge they need to manage their illnesses properly. Through an inventive and user-friendly platform, the chatbot seeks to address the pressing need for more education and awareness of VWD, enhance illness management, and offer reliable educational resources. With the use of cutting-edge technology, the chatbot makes sure that information is presented in an approachable and culturally appropriate way, which facilitates patients' comprehension and use of the advice in their everyday lives and, eventually, improves their capacity to manage VWD. © 2025 IEEE.

关键词： Automated Speech Recognition Literacy ChatBot Educational Chatbot Machine learning Senegalese Von Willebrand Disease

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