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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sakhare, Nitin N. Maruthi Shankar, B. Kalnawat, Aarti Sharma, Abha Rege, P.R. Wagh, Kapil Adhar Department of Computer Engineering BRACT’s Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, the need for advanced decision-making tools is more crucial than ever. This study explores machine learning in order to introduce an innovative method for strengthening cybersecurity protocols. The methodology involves combining Random Forests (RF) and Support Vector Machines (SVM) using the AdaBoost algorithm in an ensemble framework. The ensemble model is intricately crafted to address the complexities of intrusion detection, utilizing the combined capabilities of Random Forest’s data handling and Support Vector Machine’s pattern discrimination. AdaBoost enhances the ensemble’s predictive abilities by iteratively adjusting weights to focus more on misclassified instances. This adaptive learning process enables the model to constantly develop and adjust to new cyber threats. Validation against benchmark datasets demonstrates an impressive accuracy rate of 97.78%, highlighting the strength of the ensemble methodology. The True Positive Rate (TPR) of 98.33% demonstrates the ensemble’s effectiveness in accurately detecting and responding to potential cyber threats. The results demonstrate the superior performance of the proposed ensemble method and highlight its ability to achieve a balance between accuracy and adaptability. This research significantly advances intelligent decision-making in cybersecurity by introducing a potent ensemble approach that smoothly integrates various machine learning algorithms. The method’s proven accuracy and sensitivity make it a powerful tool in combating the increasing complexity of cyber threats. The combination of RF, SVM, and AdaBoost sets a standard for future research in strengthening digital defenses and ensuring the resilience of critical information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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A Deep Learning Approach to Analyze the Relationship Between Gender, Height, Weight, and Basal Metabolic Rate from Face Images 2

A Deep Learning Approach to Analyze the Relationship Between...

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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Siam, Shykul Islam Chowdhury, S.A.H. Ahmed, Nafi Biswas, Swapneel Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

In response to existing limitations, a novel convolutional neural network (CNN) model tailored for facial attribute estimation is introduced in this study. The methodology encompasses meticulous data preprocessing techniques, including dataset splitting, resizing, cleaning, augmentation, and cropping, to ensure the integrity of the dataset. Through rigorous real-world testing, remarkable accuracy in predicting gender, height, weight, and basal metabolic rate (BMR) from facial photos is demonstrated by our CNN architecture. Notably, unlike conventional approaches where BMR is directly estimated from facial images, three output neurons are employed by our model to simultaneously predict gender, height, and weight, which are sub-sequently utilized for BMR calculation. A primary contribution is made through the development of a comprehensive methodology that advances the reliability of facial attribute estimation by achieving an outstanding accuracy of 98.50% on our dataset named Face-ete and a commendable accuracy of 88.29% on another dataset. Our model outperforms the second-best model by percentage relative improvement factors of 2.19 and 4.28 in terms of BMR prediction accuracy for the 'Faceere' and the other dataset, respectively. This work represents a significant step forward in the field of facial attribute estimation. © 2024 IEEE.

关键词： Convolutional neural networks

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Query-attentive video summarization: a comprehensive review

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Multimedia Tools and Applications 2024年 1-40页

作者： Kadam, Bhakti D. Deshpande, Ashwini M. Department of Electronics & amp Telecommunication Engg. MKSSS’s Cummins College of Engineering for Women Pune India Department of Electronics & amp Telecommunication Engg. SCTR’s Pune Institute of Computer Technology Pune India

Since the last decade, the diverse applications of video summarization have gained increased attention, motivating researchers in the domain of computer vision to generate optimal and comprehensible video summaries. The main challenge in the research of video summarization is user perception and preference as humans are the ultimate consumers of generated summary. A single video summary cannot satisfy all users unless the summarization algorithm interacts with end users and adapts to their requirements. Conventional video summarization can not tackle the user requirements. This study explores various state-of-the-art techniques developed for generating user-intended video summaries, focusing on query-attentive video summarization. Query-attentive video summarization is a multi-modal summarization method that generates a video summary that satisfies the viewer’s requirements by taking input queries from the viewers. This paper discusses the fundamental aspects of query-attentive video summarization, tracing its progress and evolution over time. Contemporary approaches are explored in detail, highlighting developed techniques with advantages and limitations. Additionally, the article also studies publicly available datasets, including extensively utilized Query-Focused Video Summarization dataset, since these datasets ensure the validity and applicability of developed techniques. Evaluation metrics, which are essential tools for measuring performance and assessing user satisfaction are also studied and performance comparisons are presented. After investigating the domain of query-attentive video summarization, this article addresses the current research challenges and identifies potential future research objectives. This comprehensive review offers a complete guide for new researchers in the field of query-attentive video summarization, covering both existing and future real-time applications. © The Author(s), under exclusive licence to Springer Science+Business Media

关键词： Deep neural networks

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Automatic Face Mask Detection Using Deep Learning-Based Mobile-Net Architecture 6

Automatic Face Mask Detection Using Deep Learning-Based Mobi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Sandeep Rani, Shilpa Jain, Arpit Kumar, Munish Jaglan, Poonam Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Neil Gogte Institute of Technology Department of Computer Science and Engineering Hyderabad India MaaSaraswati Institute of Engineering and Technology Department of Electronics &computer Engineering Kalanaur India Panipat Institute of Engineering & Technology Department of Electronics and Communication Engineering Haryana India

ISBN: (纸本)9798350304480

COVID-19 affects public health and economies around the world. Proper wearing of masks plays a vital role in daily life to overcome this epidemic situation. To reduce human contamination, adequate people monitoring is required in public places, i.e., malls, railway stations, bus stands, markets, airports, universities, etc. In highly populated areas, manual inspection of the face mask is not easy, so an automatic face mask detection model helps detect the face mask. This paper proposed mobile Net architecture for face mask detection. This advanced architecture considers the colour image from the standard and its own database for extracting the face feature in-depth to identify the face mask. To validate our work, we used five-fold cross-validation techniques. Finally, the accuracy achieved by the proposed work on two standard databases is 96.4% and 95.7%, showing the outperforming face mask detection results. © 2023 IEEE.

关键词： Covid-19 Deep Learning Face Mask Machine Learning Mask Detection

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VocalEyes: Enhancing Environmental Perception for the Visually Impaired through Vision-Language Models and Distance-Aware Object Detection

VocalEyes: Enhancing Environmental Perception for the Visual...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Chavan, Kunal Balaji, Keertan Barigidad, Spoorti Chiluveru, Samba Raju Vellore Institute of Technology School of Electronics Engineering Vellore India S.R.M Institute of Technology Department of Computer Science and Engineering Chennai India Electrical Electronics and Communication Engineering IIT Dharwad Dharwad India

ISBN: (纸本)9798331505776

With an increasing demand for assistive technologies that promote the independence and mobility of visually impaired people, this study suggests an innovative real-time system that gives audio descriptions of a user's surroundings to improve situational awareness. The system acquires live video input and processes it with a quantized and fine-tuned Florence-2 big model, adjusted to 4-bit accuracy for efficient operation on low-power edge devices such as the NVIDIA Jetson Orin Nano. By transforming the video signal into frames with a 5 -frame latency, the model provides rapid and contextually pertinent descriptions of objects, pedestrians, and barriers, together with their estimated distances. The system employs Parler TTS Mini, a lightweight and adaptable Text-to-Speech (TTS) solution, for efficient audio feedback. It accommodates 34 distinct speaker types and enables customization of speech tone, pace, and style to suit user requirements. This study examines the quantization and fine-tuning techniques utilized to modify the Florence-2 model for this application, illustrating how the integration of a compact model architecture with a versatile TTS component improves real-time performance and user experience. The proposed system is assessed based on its accuracy, efficiency, and usefulness, providing a viable option to aid vision-impaired users in navigating their surroundings securely and successfully. © 2024 IEEE.

关键词： Speech enhancement

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The Usage of a Fast Power Switching Law in the Design of a Global Integral Terminal Sliding Mode Controller for Step up Converter in Application of Fuel Cell 5

The Usage of a Fast Power Switching Law in the Design of a G...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Akter, Farzana Shakib-Ul-Shadat, S.M. Hossain, Md. Rakib Mahedy Hasan, S.M. Datto, Sham Rajshahi University of Engineering & Technology Dept of Electronics & Telecommunication Engineering Bangladesh Rajshahi University of Engineering & Technology Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

In this research article, a nonlinear global integral terminal sliding mode controller (GITSMC) is proposed to regulate the output voltage of a step-up converter by using a Renewable energy source named as Fuel Cell. The presented controller is outlined with the knowledge of dynamic mathematical model of the boost converter. The main objective of this is to obtain a faster output voltage tracking response with less settling time. A fast power switching law is used to obtain this objective which is continuous function based. A suitable control Lyapunov function (CLF) pledges the stability of the entire system. Finally, the developed controller is carried through MATLAB/Simulink platform and the performance is assessed by comparing it with a traditional sliding mode controller and a GITSMC with traditional switching law. Simulation results clearly outperforms the proposed GITSMC over the traditional ones in regard to settling time, overshoot, undershoot and steady state tracking error. © 2023 IEEE.

关键词： Fuel cells

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Smart Border Surveillance: Real-time CNN on Drones with IoT Integration 26

Smart Border Surveillance: Real-time CNN on Drones with IoT ...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Siddique, Md. Abu Ismail Shojol, Md. Shojeb Hossain Islam, Md. Muhaiminul Alam, Samia Jyoti, Oishi Rajshahi University of Engineering & Technology Electrical & Computer Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Electronics and Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Border security is a critical priority, but solutions must balance efficacy with ethical considerations. This study examines a proposed system using convolutional neural networks (CNNs) on drone footage to identify border guards and locals by clothing types, aiming to increase security and accountability. The system integrates with ThingSpeak for real-time data collection. While employing advanced technologies like computer vision and IoT could aid border monitoring, safeguards are needed regarding privacy rights, bias prevention, and human oversight. The potential benefits of such a system, including detecting unauthorized crossings and monitoring guard activity levels, require careful weighting against risks like overreach and profiling. Further research should analyze larger social impacts and incorporate checks against errors. With deliberate multi-disciplinary development, AI-enabled border surveillance could provide valuable supplemented information to human agents, though not autonomous enforcement decisions. The suggested system provides an efficient and effective solution for border surveillance and guard accountability by utilizing cutting-edge technologies such as CNNs, computer vision, and IoT integration. © 2023 IEEE.

关键词： accountability border security CNN computer vision drone illegal activities IoT integration ThingSpeak

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A Deep Learning Approach for Classification of Vitiligo and Scar Images 10

A Deep Learning Approach for Classification of Vitiligo and ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajesh, E. Madanagopal, C. John, Reovwin Thanikaiselvan, V. Tamizharasi, T. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur613 401 India

ISBN: (纸本)9798350353068

Skin diseases such as vitiligo present significant challenges in accurate diagnosis due to their similarities with other conditions, often requiring time-consuming assessments by medical professionals. Leveraging advancements in deep learning and medical image processing, this research proposes a novel approach to efficiently and accurately classify these conditions. The proposed model comprises multiple convolutional layers for extracting intricate features from skin images, followed by max-pooling layers to downsample spatial dimensions while retaining essential information. Additionally, fully connected layers enable complex feature learning and classification. This architecture, with six convolutional layers progressively increasing in depth and spatial reduction through max-pooling, culminates in three fully connected layers for precise classification. This comprehensive design ensures effective classification between vitiligo and scar images. By utilising this architecture, our model achieves a high training accuracy of 93.53% and precision of 96.50%, demonstrating its effectiveness in discriminating between vitiligo and scar images. This approach holds promise for enhancing diagnostic accuracy and efficiency in clinical settings, contributing to improved patient outcomes and healthcare delivery. © 2024 IEEE.

关键词： Principal component analysis

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Automatic Emotion Detection from Facial Features Extraction and Machine Learning Models

Automatic Emotion Detection from Facial Features Extraction ...

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2025 IEEE International Students' Conference on Electrical, electronics and computer Science, SCEECS 2025

作者： Shankar, Raghav Singh Kirar, Bhupendra Upadhyay, Pratham Jee Mishra, Shree Govind Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Information Technology Indian Institute of Information Technology Bhopal India

ISBN: (纸本)9798331529833

In the domain of Interactive Systems (IS), machine learning (ML) emerges as a profound, user-friendly, and accurate approach to enhancing emotional intelligence in digital systems. This research work focuses on facial emotion detection, an important but often-neglected aspect of affective computing. Models used for the assessment include Multinomial Naïve Bayes (MNB), Decision Trees (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Machines (SVMs), XG Boost (XGB), and Logistic Regression (LR). By employing the FER2013 dataset and MediaPipe's (MP's) facial recognition technology, this research extracts 478 3D facial landmarks to test. The best Acc in emotion detection is found using LR with an accuracy of 65.57% followed by XGB with an accuracy of 61.24%. This study thus will help as a practical application toward making improvements in IS in matters regarding health, marketing, or even security. © 2025 IEEE.

关键词： Adversarial machine learning

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