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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Raha, Avi Deb Gain, Mrityunjoy Saha, Sudip Kumar Rahman, Md Shohanur Adhikary, Apurba Debnath, Rameswar Bairagi, Anupam Kumar Biswas, Sujit Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh KUET Dept. of Mechatronics Engineering Khulna Bangladesh Noakhali Science and Technology University Dept. of ICE Noakhali Bangladesh City University of London Computer Science Department EC1V 0HB United Kingdom

ISBN: (纸本)9798331531195

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes. © 2025 IEEE.

关键词： Distillation equipment

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Advanced Optimized Counter based Hierarchal Model to Predict Cancer's Disease from Cancer Patients Neurological Features

Advanced Optimized Counter based Hierarchal Model to Predict...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Laxminarayanamma, K. Krishnaiah, R.V. Sammulal, P. Institute of Aeronautical Engineering Dept. of Information Technology Hyderabad500043 India Electronics and Communication Engineering AP Guntur522212 India Jntuh Computer Science and Engineering Kondagattu Karimnagar Telangana Jagtial505501 India

ISBN: (纸本)9781665474511

Cancer disease prediction based on neurological characteristics of cancer patients is gaining a significant research attention in recent times. The role of data in the processing and analysis of neurological features is critical, and the main goal is to efficiently extract neurological features from cancer patients' data. Random extraction of neurological features from cancer patient data is a new research initiative. Convolutional Neural Networks (CNN) is a promising approach in various healthcare applications to efficiently perform the data processing tasks. Some CNN-based approaches have been proposed to perform efficient cancer disease prediction using remotely sensed neurological features. Cancer disease extraction based on MPDCNN is one of the best CNN approaches used for extracting features and perform disease prediction from Geo-Fan-2 (GF-2) sensing cancer patient data. However, due to its sparse arrangement of optimal boundary, exact neurological features and high amount of training time, it is insufficient to investigate and automate the neurological feature extraction process from the cancer patient's data. A Novel Optimized Multi Feature Contour based Hierarchical Neural Network (NOMFCHNN) is proposed to improve the automatic neurological feature prediction process. NOMFCHNN is made up of expanding neural network features and layers related to inception, which contains the data about network localization, and this approach uses optimal and exact neurological feature matching with extended feature extraction. This method also employs contour map optimization to identify contours based on globalization of cancer patient data along with the output of the identified contour being transmitted to the next identified contour in the selected hierarchical region. Furthermore, the proposed approach evaluates the low- resolution term in cancer patient's data to gain knowledge from the cancer patient's data by obtaining the prediction results of neighbouring optimal

关键词： Feature extraction

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A study on the E-commerce Trends using Data Analysis 3

A study on the E-commerce Trends using Data Analysis

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3rd International Conference on Innovations in computer science and Software engineering, ICONICS 2022

作者： Peter Noronha, Joseph Terence Mahmood Khan, Shariq Sebastian, Stina Nabeel Mustafa, Syed Muhammad NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Dept. of Business Management Riyadh Saudi Arabia

ISBN: (纸本)9781665461412

Over the years, numerous businesses transitioned to creating online access, which helped them towards gaining access to a global presence. With the rise of ecommerce businesses accessed through websites or social media platforms, many seemed to have failed to generate sufficient revenue for themselves. Machine learning, artificial intelligence, and data science have aided retailers in managing their supplies, creating a brand for themselves with the review system, and providing the customer with recommendations. Moreover, with the help of these recommendations, customers have made the right choices when purchasing a product. Thus, this is what all established retailers and their customers demonstrate. Due to a massive increase in e-commerce businesses, it is significant to have details of all the ongoing strategies in the current business to ensure a reasonable growth rate. This study will interpret the different trends, platforms, and strategies involved in an ecommerce business. © 2022 IEEE.

关键词： Electronic commerce

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A Hybrid Distance Technique to Optimize the K-Nearest Neighbor Algorithm 2

A Hybrid Distance Technique to Optimize the K-Nearest Neighb...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmuttaleb Al-Musawi, Hussein Ali Aljaidi, Mohammad Sara, Massar Boostani, Reza School of Engineering Shiraz University Electrical and Computer Engineering Faculty Cse and It Dept. Shiraz Iran Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Plenty of Bayesian-consistent, heuristic and statistical classifiers have been introduced. Although support vector machines, deep neural networks, and random forests exhibit acceptable performance in several fields, their primary challenge lies in the lengthy training phase, especially when dealing with big data. K-nearest neighbors (KNN) is one of the simplest, Bayesian consistent and most common algorithms for classification tasks which locally makes its decision and there is no need to any training phase, can be implemented by parallel processing algorithms which makes it suitable to deal with big datasets. The core of this classifier relies on measuring the distance between test samples and its neighbors. Therefore, designing a hybrid distance technique to better find relevant neighbors is of interest. In this research, a weighted combination five distances has been proposed to optimize the performance of the KNN algorithm on diverse 28 datasets. To select appropriate weights of distances for each dataset, particle swarm optimization algorithm was used due to its simplicity and high convergence speed. The proposed method was applied to the datasets, demonstrating acceptable performance compared to its rivals. © 2023 IEEE.

关键词： Classification (of information)

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User Authentication on Smart Speakers Leveraging Acoustic Imaging

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IEEE Transactions on Mobile Computing 2025年

作者： Ren, Yanzhi Xia, Zhiliang Li, Siyi Liu, Hongbo Yu, Jiadi Li, Shuai Li, Hongwei University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China Shanghai Jiao Tong University Dept. of Computer Science and Engineering Shanghai200240 China University of Oulu Finland

The user authentication has drawn increasingly attention as the smart speaker becomes more prevalent. For example, smart speakers that can verify who is sending voice commands can mitigate various types of attacks such as replay attack or impersonation attack. Existing user authentication solutions either cannot be applicable to smart speakers directly or require certain additional user-device interaction or pre-installed infrastructure, which may severely affect the user experience and create extra burdens to users. In this work, we propose a user authentication system utilizing acoustic images, which are derived from the smart speaker by emitting beep signals and sensing echoes from the user's body with its microphone array, as the proof for user authentication. Given the acoustic samplings of the reflected beep signal, our system designs a distance estimation component by applying a correlation based technique on the beamformed signal to estimate the distance between the user and microphone array. Our image construction component then constructs a virtual imaging plane using the estimated distance and steers the array towards each grid of the plane to generate an acoustic image of the user. We also propose a transfer learning-based method to derive efficient features from the constructed images, and employ SVM classifiers for accurate user authentication. Moreover, to ensure the accuracy of our system, our user direction estimation scheme could further estimate the DoA of the user's voice command to make sure that the user stands in front of the smart speaker during the authentication process. Our extensive experiments demonstrate that our system is robust and accurate across various scenarios. © 2002-2012 IEEE.

关键词： Acoustic imaging

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AI-Based Thermal Video Analysis in Privacy-Preserving Healthcare: A Case Study on Detecting Time of Birth 22

AI-Based Thermal Video Analysis in Privacy-Preserving Health...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： García-Torres, Jorge Meinich-Bache, Øyvind Rettedal, Siren Engan, Kjersti University of Stavanger Dept. of Electrical Engineering and Computer Science Norway Laerdal Medical As Stavanger Norway Stavanger University Hospital Dept. for Simulation-based Learning Norway University of Stavanger Faculty of Health Sciences Norway

ISBN: (纸本)9798331520526

Approximately 10% of newborns need some assistance to start breathing and 5% proper ventilation. It is crucial that interventions are initiated as soon as possible after birth. Accurate documentation of Time of Birth (ToB) is thereby essential for documenting and improving newborn resuscitation performance. However, current clinical practices rely on manual recording of ToB, typically with minute precision. In this study, we present an AI-driven, video-based system for automated ToB detection using thermal imaging, designed to preserve the privacy of healthcare providers and mothers by avoiding the use of identifiable visual data. Our approach achieves 91.4% precision and 97.4% recall in detecting ToB within thermal video clips during performance evaluation. Additionally, our system successfully identifies ToB in 96% of test cases with an absolute median deviation of 1 second compared to manual annotations. This method offers a reliable solution for improving ToB documentation and enhancing newborn resuscitation outcomes. © 2025 IEEE.

关键词： Deep learning Newborn resuscitation Privacy-preserving Thermal video Time of birth detection

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Analysis and Ranking Resume using Machine Learning Algorithms and NLP

Analysis and Ranking Resume using Machine Learning Algorithm...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratik Chandrasen Deshmukh M. R. Bendre Dept. of Data Science Engineering Pravara Rural Engineering College Loni India Dept. of Computer Engineering Pravara Rural Engineering College Loni India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited.

关键词： Machine learning algorithms Resumes Employment Natural languages Machine learning Software Portals Recruitment Testing Qualifications

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Evaluating Large Language Models for Automatic Register Transfer Logic Generation via High-Level Synthesis

arXiv

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arXiv 2024年

作者： Swaroopa, Sneha Mukherjee, Rijoy Debnath, Anushka Chakraborty, Rajat Subhra The Dept. of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India The Dept. of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India

The ever-growing popularity of large language models (LLMs) has resulted in their increasing adoption for hardware design and verification. Prior research has attempted to assess the capability of LLMs to automate digital hardware design by producing superior-quality Register Transfer Logic (RTL) descriptions, particularly in Verilog. However, these tests have revealed that Verilog code production using LLMs at current state-of-the-art lack sufficient functional correctness to be practically viable, compared to automatic generation of programs in general-purpose programming languages such as C, C++, Python, etc. With this as the key insight, in this paper we assess the performance of a two-stage software pipeline for automated Verilog RTL generation: LLM based automatic generation of annotated C++ code suitable for high-level synthesis (HLS), followed by HLS to generate Verilog RTL. We have benchmarked the performance of our proposed scheme using the open-source VerilogEval dataset, for four different industry-scale LLMs, and the Vitis HLS tool. Our experimental results demonstrate that our two-step technique substantially outperforms previous proposed techniques of direct Verilog RTL generation by LLMs in terms of average functional correctness rates, reaching score of 0.86 in pass@1 metric. Copyright © 2024, The Authors. All rights reserved.

关键词： C++ (programming language)

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Differential Diagnosis of Lung Disease through Quantitative Lung Ultrasound Spectroscopy, an in vivo clinical study over 114 patients

Differential Diagnosis of Lung Disease through Quantitative ...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Mento, Federico Perpenti, Mattia Barcellona, Giuliana Perrone, Tiziano Demi, Libertario Dept. of Information Engineering and Computer Science Ultrasound Laboratory Trento University of Trento Trento Italy Emergency and Urgency Department Humanitas Gavazzeni Bergamo Bergamo Italy

ISBN: (纸本)9798350346459

Lung ultrasound (LUS) represents an important imaging tool to evaluate the state of the lung surface. However, it is mainly based on the visual interpretation of imaging artifacts, especially the vertical ones. This subjective interpretation leads to poorly reproducible approaches, and, thus, qualitative diagnoses. Therefore, in this study, we present a quantitative multifrequency analysis based on the parameterization of vertical artifacts. Specifically, radiofrequency (RF) data were acquired from 114 patients affected by different diseases using a linear and a convex probe, connected to the ULtrasound Advanced Open Platform (ULA-OP). The vertical artifacts observed in patients affected by pneumonia, cardiogenic pulmonary edema (CPE), and pulmonary fibrosis (PF) were extracted and analyzed. Specifically, the significance of three features (native frequency, bandwidth, and total intensity) in discriminating these diseases was evaluated. The results show how the total intensity seems significant in differentiating PF from the other two diseases (p-value © 2023 IEEE.

关键词： Ultrasonics

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Home Care Assistance Solution based on a Software Multi-Agents system 24

Home Care Assistance Solution based on a Software Multi-Agen...

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24th International Conference on Control systems and computer science, CSCS 2023

作者： Rasras, Mohammad Marin, Iuliana Radu, Serban University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania University Politehnica of Bucharest Dept. of Engineering in Foreign Languages Bucharest Romania University Politehnica of Bucharest Dept. of Computer Science Bucharest Romania

ISBN: (纸本)9798350313390

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ' age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home's residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies. © 2023 IEEE.

关键词： Multi agent systems

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