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Advancements in Operational Transconductance Amplifiers (OTAs) for Design, Applications & Performance Optimization Techniques with Hardware Implementation 3

Advancements in Operational Transconductance Amplifiers (OTA...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Pramath, V. Pavithra, G. Ninawe, Swapnil S. Manjunath, T.C. Bengaluru India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Rajarajeswari College of Engineering Department of Computer Science & Engineering IoT Cyber Security & Block Chain Technology Karnataka Bangalore India

ISBN: (纸本)9798350374643

This research paper presents the design and simulation of a novel architecture for Multiple Path Fully Differential Operational Transconductance Amplifiers (OTAs) with a Dual Flipped Voltage Follower, specifically developed for VLSI-based signal processing applications. Utilizing advanced 50nm CMOS technology and Cadence tools, the proposed OTA architecture achieves enhanced performance and versatility in handling various signal processing tasks. Design employs multiple signal paths, which contribute to improvements in bandwidth, gain, and linearity, while also effectively minimizing common-mode noise. This multi-path approach ensures better signal integrity and reliability in high-performance applications. The integration of the Dual Flipped Voltage Follower further strengthens the OTA's stability and dynamic range, allowing it to operate seamlessly in high-speed VLSI systems. The architecture is designed to cater to the rigorous demands of modern signal processing, where precision and speed are critical. Extensive simulation studies were conducted to validate the OTA design, and the results demonstrate that the proposed approach meets or exceeds the stringent requirements of today's signal processing applications. Findings of this research contribute to the ongoing advancements in VLSI design methodologies and provide a promising solution for engineers working with nanoscale CMOS technologies. Simulation results using Cadence tools highlights effectiveness and robustness of proposed design. © 2024 IEEE.

关键词： CMOS integrated circuits

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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A Review of Intrusion Detection for Internet of Things Using Machine Learning

A Review of Intrusion Detection for Internet of Things Using...

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2024 International Conference on cybernation and Computation, cyberCOM 2024

作者： Paul, Ekwueme Chika Amrita Sharda School of Engineering & Technology Sharda University Computer Science & Engineering UP Greater Noida India Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University UP Greater Noida India

ISBN: (纸本)9798331542344

As Internet of Things (IoT) networks proliferate across consumer and industrial sectors, safeguarding these interconnected cyber-physical systems is imperative. IoT ecosystems present abundant targets for malicious intrusions including denial-of-service, man-in-the-middle, crypto and access attacks due to their scale and dynamic topology. Such intrusions can severely disrupt sensing, monitoring and control functions, enabling catastrophic cascading failures. Consequently, IoT platforms require intelligent intrusion detection system (IDS) to identify anomalous behaviors and known attack patterns. Conventional signature-based detection relying on expert rules proves inadequate against evolving attacks. In contrast, data-driven machine learning (ML) algorithms offer versatile capabilities for securing IoT by modeling normal vs anomalous traffic and information flows based on supervised, semi-supervised or unsupervised learning. Critical analysis reveals ML-driven intrusion detection frameworks achieve high accuracy in controlled settings. However, challenges related to computational burdens, adversarial attacks, concept drift and lack of model standardization need resolution before widespread field deployment. As IoT ecosystems continue proliferating amidst escalating threats, our systematic review helps identify limitations and promising directions for future research on ML-powered IDSs. This paper presents a comprehensive analysis of adopting ML techniques like neural networks, random forest, support vector machines, ensemble models and deep learning architectures to engineer anomaly detection and misuse detection modules tailored for IoT environments. © 2024 IEEE.

关键词： Network intrusion

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Combating Terrorism: Detecting and Analyzing Extremist Content on X (Formerly Twitter) 3

Combating Terrorism: Detecting and Analyzing Extremist Conte...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Mwendwa, Gideon Chouhan, Lokesh Etyang, Felix Mandela, Ngaira Gift, Uwineza School of Cyber Security and Digital Forensics National Forensic Sciences University Goa India Cochin University of Science and Technology School of Engineering Department of Computer Science Cochin India School of Cyber Security and Digital Forensics National Forensic Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensic Sciences University Jinja Uganda

ISBN: (纸本)9798331518592

This study investigates the efficacy of machine learning algorithms in detecting and evaluating extremist content on social media platforms, and we employ X, formerly known as Twitter. We therefore utilize Kaggle's dataset that contains 83,000 entries from both extremist and non-extremist posts to compare the performance of four machine learning models, namely Support Vector Machine (SVM), Random Forest, Gradient Boosting, and Naive Bayes. Moreover, we examine a Stacking Classifier that leverages the advantages of all four models. The models were assessed using accuracy, precision, recall, F1 Score, ROC-AUC Score, training duration, and prediction time. The findings of our study revealed that the Support Vector Machine (SVM) algorithm was the most accurate and resilient, whereas Naive Bayes showcased remarkable computing efficiency, rendering it well-suited for real-time detection. The Random Forest and Gradient Boosting algorithms offered a well-balanced approach. Notably, the novel Stacking Classifier outperformed individual models, demonstrating the strength of a hybrid methodology. Hence our study presents a thorough structure for improving the identification of extremist material on social media, thereby providing essential knowledge for creating efficient, immediate detection systems to combat online extremism successfully. © 2024 IEEE.

关键词： Tweets

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Data Analytic for Healthcare cyber Physical System

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IEEE Transactions on Network Science and engineering 2023年第5期10卷 2490-2502页

作者： Gan, Wensheng Hu, Kaixia Huang, Gengsen Chien, Wei-Che Chao, Han-Chieh Meng, Weizhi Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan National Dong Hwa University Department of Electrical Engineering Hualien97401 Taiwan UCSI University Institute of Computer Science and Innovation Kuala Lumpur Malaysia Department of Applied Mathematics and Computer Science Cyber Security Section Lyngby2800 Denmark

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data. © 2013 IEEE.

关键词： Data mining

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Advancing Stance Detection of Political Fan Pages: A Multimodal Approach 24

Advancing Stance Detection of Political Fan Pages: A Multimo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Kuo, Kuan-Hung Wang, Ming-Hung Kao, Hung-Yu Dai, Yu-Chen National Chung Cheng University Department of Computer Science and Information Engineering Chiayi Taiwan National Institute of Cyber Security Taipei Taiwan

ISBN: (纸本)9798400701726

The evolution of political campaigns is evident with the ascent of social media. Ideological beliefs are increasingly disseminated through political-affiliated fan pages. The interaction between politicians and the general public on these platforms plays a pivotal role in election outcomes. In this study, we utilize a multimodal approach to explore and quantify similarities of ideologies among political fan pages. we employed visualization techniques to demonstrate the political stance of each fan page. To validate our proposal, we concentrated on an analysis of the 2021 national referendums in Taiwan, encompassing a collection of fan pages and their corresponding posts that were related to these referendums. Through a qualitative analysis of the content of these fan pages, the efficacy of our multimodal framework in clustering fan pages according to their respective political ideologies was evaluated. The findings of this study underscore the significant enhancement in the accuracy of stance detection when integrating multiple modalities of data, namely textual content, visual imagery, and user interactions. © 2024 Copyright held by the owner/author(s).

关键词： Image enhancement

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IoT-Based Plant Health Monitor Using NodeMCU and ESP8266 5

IoT-Based Plant Health Monitor Using NodeMCU and ESP8266

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tandel, Rajat Ebenezer, V. Santhiya, P. Edwin, Bijolin Stewart Kirubakaran, S. Thanka, Roshni Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India

ISBN: (纸本)9798331518097

Plant Monitor Using NodeMCU(ESP8266)is a technology that is advanced;it uses IoT-Internet of Things to measure and monitor different plant phenomena. This system, which is based on sensors that collect data on different factors such as soil type, temperature, humidity, and other vital metrics vital for plant growth, is also integrated. Bringing IoT into the picture, the system can remotely sense the metrics and having control over other factors which include watering, shading, and even security provision for the plants to make it thrive. Besides that, it will also send notifications to users, hence allowing them to engage in the monitoring process through mobile applications with support from their smartphones. The Smart Plant Monitoring System though creates an impact on the efficiency of plant care also impacts on water conservation, automation of plant care process and better agricultural practices. © 2024 IEEE.

关键词： Efficiency

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Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Jamandlamudi, Sahit Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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