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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

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Optimizing Sheep Breed Classification with Bat Algorithm-Tuned CNN Hyperparameters

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SN computer science 2024年第2期5卷 219页

作者： Ravikiran, H.K. Jayanth, J. Sathisha, M.S. Bindu, K. Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering GSSSIETW Karnataka Mysuru 570016 India Department of Computer Science and Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering Global Academy of Technology Karnataka Bengaluru 560098 India

Efficiently classifying sheep breeds through image analysis is pivotal in modern animal husbandry, influencing critical management and breeding decisions. This study delves into automating this process by harnessing Convolutional Neural Networks (CNNs), with a particular focus on optimizing key hyperparameters—the learning rate and dropout rate—essential for refining model performance. Manual hyperparameter tuning is often time-consuming and demands expertise. To overcome this challenge, we introduce an innovative approach that utilises the Bat algorithm, a bio-inspired optimization technique. This algorithm mimics bat echolocation behaviors, skillfully navigating the complex hyperparameter search space to determine optimal values. By dynamically adjusting CNN hyperparameters, our research aims to boost classification accuracy while simplifying the tuning process. Empirical results highlight significant gains in classification accuracy and emphasizes the Bat algorithm's efficacy. The optimized CNN model, empowered by fine-tuned hyperparameters, demonstrates superior performance, promising practical applicability in real-world sheep breed classification scenarios. This study meticulously adjusts pulse rate and loudness, revealing an optimal combination of [0.001, 0.24404868], which substantially improves model performance. The findings emphasize the Bat algorithm's role in streamlining hyperparameter tuning and its potential impact on automated sheep breed classification. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BAT algorithm Classification CNN Dropout Hyperparameter tuning Learning rate

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Fingerprint Matching for Noisy and Distorted Patterns Using a Siamese Network With ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Kumar Lehana, Parveen Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii Ali Alzubi, Ahmad University of Jammu DSP Laboratory Department of Electronics Jammu180006 India University of Jammu Department of Computer Science and IT Jammu180006 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab Rajpura140401 India Manipal University Jaipur Department of Computer Science & Communication Engineering Jaipur303007 India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa42-200 Poland Faculty of Informatics and Software Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv03056 Ukraine King Saud University Community College Department of Computer Science Riyadh11451 Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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An Empirical Study of Unanswered Python-Related Questions on Stack Overflow

An Empirical Study of Unanswered Python-Related Questions on...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Nugroho, Yusuf Sulistyo Halim, Salsabil Aisyah Abdul Islam, Syful Kurniawan, Yogiek Indra Erlina, Tati Universitas Muhammadiyah Surakarta Informatics Engineering Faculty of Communication and Informatics Surakarta Indonesia BSMRSTU Computer Science and Engineering Faculty of Engineering Gopalganj Bangladesh Universitas Jenderal Soedirman Informatics Engineering Faculty of Engineering Purwokerto Indonesia Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350376210

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users (72.52%), followed by mid-reputation users (17.93%), and high-reputation users (9.5 5%). The distribution of the unanswered question types shows that low-reputation users ask more 'how' type of question (38.90%) followed by mid and high-reputation users. In contrast, the 'what' and 'why' questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement. © 2024 IEEE.

关键词： Python

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A nano-scale arithmetic and logic unit using a reversible logic and quantum-dots

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Journal of Supercomputing 2024年第1期80卷 395-412页

作者： Navimipour, Nima Jafari Ahmadpour, Seyed-Sajad Yalcin, Senay Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Computer Engineering Nisantasi University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

The arithmetic and logic unit (ALU) is a key element of complex circuits and an intrinsic part of the most widely recognized complex circuits in digital signal processing. Also, recent attention has been brought to reversible logic and quantum-dot cellular automata (QCA) because of their intrinsic capacity to decrease energy dissipation, which is a crucial need for low-power digital circuits. QCA will be the preferred technology for developing the subsequent generation of digital systems. These technologies played a substantial role in the design of the ALU for operations such as multiplication, subtraction, and division. In developing reversible logic and QCA technologies, the ALU is frequently studied as a central unit. Implementing an efficient ALU with low quantum cost and a small number of cells based on an efficient reversible block can solve all previous issues. Therefore, this research constructs a Feynman gate, a Fredkin gate, and full adder circuits using reversible logic and QCA technology. Using all of the specified circuits, a 20-operation ALU is constructed. The power consumption of the proposed ALU under various energy ranges demonstrated significant improvements over earlier designs. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Energy utilization

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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

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Intelligent Automation & Soft Computing 2023年第3期35卷 3501-3515页

作者： M.Daniel Nareshkumar B.Jaison Department of Electronics and Communication Engineering R.M.K.Engineering CollegeKavaraipettai601206India Department of Computer Science and Engineering R.M.K.Engineering CollegeKavaraipettai601206India

With advancements in computing powers and the overall quality of images captured on everyday cameras,a much wider range of possibilities has opened in various *** fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much *** than ever before,there is a plethora of info about sign language usage in the real *** languages,and by extension the datasets available,are of two forms,isolated sign language and continuous sign *** main difference between the two types is that in isolated sign language,the hand signs cover individual letters of the *** continuous sign language,entire words’hand signs are *** paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language(ASL).The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classiﬁcation accuracy on the data,thereby showing that interpreters can be implemented in the real *** newly proposed Mobile-NetV2 architecture serves as the backbone of this *** is designed to run on end devices like mobile phones and infer signals(what does it infer)from images in a relatively short amount of *** the proposed architecture in this paper,the classiﬁcation accuracy of 98.77%in the Indian Sign Language(ISL)and American Sign Language(ASL)is achieved,outperforming the existing state-of-the-art systems.

关键词： Deep learning machine learning classiﬁcation ﬁlters american sign language

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MASSFormer: Mobility-Aware Spectrum Sensing Using Transformer-Driven Tiered Structure

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IEEE communications Letters 2025年第6期29卷 1220-1224页

作者： Janu, Dimpal Mushtaq, Faisel Mandia, Sandeep Singh, Kuldeep Kumar, Sandeep Malaviya National Institute of Technology Jaipur Electronics and Communication Engineering Department Jaipur302017 India SRM University AP Electronics and Communication Engineering Department Amaravati522502 India Malaviya National Institute of Technology Jaipur Department of Computer Science and Engineering Jaipur302017 India Thapar Institute of Engineering & Technology Department of Electronics and Communication Engineering Patiala147004 India Central Research Laboratory Bharat Electronics Ltd. Ghaziabad201010 India

In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based cooperative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, allowing the proposed method to model the temporal dynamics of user mobility by effectively capturing long-range dependencies. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU. It processes them in parallel using the SU-transformer to learn the spatio-temporal features at SU-level. Subsequently, the collaborative transformer learns the group-level PU state from all SU-level feature representations. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with simulation results that demonstrate its higher performance compared to existing methods in terms of detection probability Pd, sensing error, and classification accuracy (CA). © 1997-2012 IEEE.

关键词： Fading channels

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Deep Learning Based Navigation System for Train Line Equipment Inspection Using Drones 9

Deep Learning Based Navigation System for Train Line Equipme...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Hosono, Yuki Surantha, Nico Graduate School of Science and Engineering Tokyo City University Department of Electrical Electronics and Communication Engineering Tokyo Japan Tokyo City University Faculty of Science and Engineering Department of Electrical Electronics and Communication Engineering Tokyo Japan

ISBN: (纸本)9798331510732

Early damage detection and regular train overhead line equipment inspection are essential for safe and reliable train operation. Traditionally, these inspections have been conducted directly by power line engineers at night. However, such methods face challenges like a shrinking workforce and limited mobility. Attempts have been made to enhance mobility by installing cameras on test vehicles, yet the analysis of captured images still requires manual intervention. With the advancement of drones, high-resolution cameras, and deep learning technology, AI-based inspections using drones have become a promising solution. This study aims to develop an AI-based drone navigation system for automated inspection of train overhead line equipment, focusing on improving inspection efficiency and accuracy through model training for future drone deployment. In this study, a simulator was employed to generate images of overhead line facilities, and a machine learning model was trained using these images, based on the concepts of Dronet and FlowDronet from previous studies. The model's performance was evaluated using varying numbers of training images. A test dataset was created from images captured by a drone in flight and processed with optical flow techniques. The results revealed that the model trained with 22000 images achieved the highest performance, with an Average accuracy of 87.90%, a Collision Recall of 89.60%, and a Non-Collision Recall of 85.99%. These findings underscore the importance of larger datasets in enhancing model generalization capabilities and demonstrate the effectiveness of utilizing drones and AI for automated inspection of train overhead line equipment. © 2024 IEEE.

关键词： Optical flows

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A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

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Iran Journal of computer science 2024年第3期7卷 663-698页

作者： Prity, Farida Siddiqi Hossain, Md. Maruf Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Department of Computer Science and Engineering Shanto-Mariam University of Creative Technology Dhaka 1230 Bangladesh

Cloud computing is a robust paradigm that empowers users and organizations to procure services tailored to their needs. This model encompasses many offerings, including storage solutions, platforms for seamless deployment, and convenient access to web services. Load balancing, a fundamental pillar in cloud computing, is crucial in distributing requests across multiple servers to optimize resource utilization and reduce response times. However, load balancing presents a common challenge in the cloud environment, as it hampers the ability to maintain optimal application performance while adhering to the stringent requirements of Quality of Service (QoS) measurements and Service Level Agreement (SLA) compliance mandated by cloud providers to enterprises. The equitable workload distribution across servers poses a significant challenge for cloud providers. Hence, an efficient load-balancing technique should optimize resource utilization in Virtual Machines (VMs) to ensure maximum user satisfaction and overall system efficiency. However, existing review papers on load balancing in cloud environments often exhibit limitations, lacking in-depth analyses, graphical representations, and comprehensive evaluations of performance metrics. This review paper aims to fill these gaps by providing a novel taxonomy of load balancing algorithms divided into four categories (types of algorithms, nature of problem, metrics, and simulation tools) and thoroughly examining their objectives, parameters, and operational flows. It evaluates the strengths and weaknesses of these algorithms, considering their nature and type, and employs qualitative QoS parameter-based criteria for effectiveness evaluation. The paper also includes a comparative analysis of simulation tools, visual representations, and experimental results. By offering valuable insights, open issues, recommendations, and future directions, this review paper equips researchers, practitioners, and cloud service providers with the k

关键词： Algorithms Cloud computing Load balancing Taxonomy

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