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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwavidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of North Texas Denton Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350385199

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model's generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network's ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model's accuracy by 7%, achieving an overall accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening © 2024 IEEE.

关键词： Generative adversarial networks

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Evolutionary Algorithms and Fuzzy Systems for Enabling Smart Technologies across Industries 1

Evolutionary Algorithms and Fuzzy Systems for Enabling Smart...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Refonaa, J. Poornima, D. Shabu, S.L. Jany Kanipriya, M. Surya, V. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India Srmist Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Smart technology have end up an increasing number of vital in today's rapidly evolving generation panorama. Automation, records-pushed decision-making, and streamlined operations are all being revolutionized by means of the usage of clever era. However, there are some of boundaries that save you their effective deployment, which include as converting conditions, complicated device interactions, and the requirement for instantaneous flexibility. Fuzzy Logic Control for Smart Technology Optimization (FLC-STO) is a way that makes use of the complementary nature of Evolutionary Algorithms and Fuzzy Systems (EA-FS) to achieve optimization in smart era. FLC-STO is a dynamic and adaptive technique for optimizing smart generation for maximum performance over a great form of situations. FLC-STO has proven to be powerful via simulation evaluation in optimizing smart generation in practical settings. It demonstrates how the proposed method works in workout, demonstrating how common performance, performance, and flexibility are all improved in the face of dynamic surroundings and complex interactions. The FLC-STO method gives a effective framework for permitting and optimizing clever generation throughout industries due to its fusion of Evolutionary Algorithms and Fuzzy Systems. The studies presented here allows to enhance clever technology and encourage their fashionable reputation with the aid of addressing the difficulties confronted with the aid of converting contexts, complex gadget interconnections, and the need for actual-time variation. © 2023 IEEE.

关键词： Evolutionary algorithms

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics 5

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Chougala, Manjula R Sushma, B. Bhayyar, Akshata S Bhandarkar, Abhay Pavithra, P.N. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331528539

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development. © 2024 IEEE.

关键词： Educational robots

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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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EDRLNet: privacy preservation in online social networks via deep reinforcement learning and graph convolutional networks

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International Journal of Information Technology (Singapore) 2025年 1-10页

作者： Gowda, Nithish Ranjan Venkatesh Venugopal, K.R. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Associate Professor Department Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India University Visvesvaraya College of Engineering Bangalore University Bangalore India

Online social networks (OSN) have become extremely popular in the past few decades. Prominent OSN companies, such as Facebook and Twitter, control a huge amount of information on its users as well as interactions that may cause people to have major privacy concerns. The OSN information is combined with other online sources to reveal sensitive information. As a result, there is a great need to anonymize the OSN data before providing it to a third party. Thus, in this study, we propose a Deep Reinforcement Learning (DRL)-based rewiring operation that entails changing the relationships (edges) between nodes (users) without changing their degree to protect users’ privacy. To accomplish this, we introduce a novel technique for Enhanced DRL Networks called “EDRLNet” which combines an Optimized Policy Network (OPN) and Graph Convolutional Network (GCN) that preserves the essential characteristics of OSN data while anonymizing it to prevent privacy breaches. This approach employs a GCN to extract embeddings (features) from the edges and nodes. Furthermore, OPN applies the graph feature representation learned by the GCN model to policy learning. Additionally, the hyperparameters of OPN are optimized by using the Addax optimization algorithm (AOA). The suggested scheme’s performance is assessed using PageRank with mean-squared error (MSE), Root mean-squared error (RMSE), mean absolute error (MAE), Privacy score, Utility Score, Clustering coefficient, privacy and information loss. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Addax optimization algorithm Anonymization Deep reinforcement learning Graph convolutional network Online social network Privacy preservation

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AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

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science China(Information sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

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An Overview of Artificial Intelligence Diagnostic Systems for Hepatitis B Virus 4

An Overview of Artificial Intelligence Diagnostic Systems fo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Singh, Dalwinder Kaur, Harjeet School of Computer Science and Engineering Lovely Professional University Punjab India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350387490

A virus that can attack a human's liver and cause serious liver damage is primarily known as Hepatitis B Virus or HVB. It influences nearly all functionalities performed by the liver. It is basically a short term infection;however, it will become a chronic disorder and remains for a long term in some individuals. The severe effect of this illness is that it can cause chronic liver diseases, and with time it results in cancer of the liver. Hence, it puts a great risk to the life of a patient. The early detection of the virus is necessary, which assists in saving the patient's life and brings more treatment procedures that can adapt by the doctor for treatment. However, this life-threatening disease's therapy and diagnosis are both costly and can have dangerous adverse effects. As a result, developing a diagnostic system that lowers the cost of therapy and diagnosis is critical. In the medical field, several artificial intelligence technologies have been employed to assist doctors and experts in the diagnosing process. The significant aim of this paper is to thoroughly review all these diagnostic systems developed or proposed by various researchers. Later on, the systems are compared with one another to evaluate which system or model is best to use in solving the problem. The parameter that is taken into account to complete the comparison. As a result, it is noticed that the system developed by using a genetic neural network is accurate for the diagnosis of HBV and has an accuracy of 99.14%. © 2024 IEEE.

关键词： Artificial Intelligence Comparative Study Diagnostic Systems Early Detection Hepatitis B Virus Intelligent Systems Medical Expert Systems

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Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

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computer Systems science & engineering 2023年第3期44卷 2293-2307页

作者： B.Murugeshwari S.Rajalakshmi K.Sudharson Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India Department of Information Technology Velammal Institute of TechnologyChennai601204India

Imagine numerous clients,each with personal data;individual inputs are severely corrupt,and a server only concerns the collective,statistically essential facets of this *** several data mining methods,privacy has become highly *** a result,various privacy-preserving data analysis technologies have ***,we use the randomization process to reconstruct composite data attributes ***,we use privacy measures to estimate how much deception is required to guarantee *** are several viable privacy protections;however,determining which one is the best is still a work in *** paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data ***-more,this paper investigates the use of arbitrary nature with perturbations in privacy *** to the research,arbitrary objects(most notably random matrices)have"predicted"frequency *** shows how to recover crucial information from a sample damaged by a random number using an arbi-trary lattice spectral selection *** system's conceptual frame-work posits,and extensive practicalfindings indicate that sparse data distortions preserve relatively modest privacy protection in various *** a result,the research framework is efficient and effective in maintaining data privacy and security.

关键词： Data mining data privacy arbitrariness data security perturbation

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A Deep CNN-Based Approach for Revolutionizing Bengali Handwritten Numeral Recognition 2nd

A Deep CNN-Based Approach for Revolutionizing Bengali Handwr...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Islam, Sudipta Progga Parvin, Farjana Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

Recognition of Bengali handwritten digits is a fascinating and demanding research problem that has garnered significant interest from researchers in the fields of pattern recognition. In this paper, a task-oriented deep convolutional architecture for recognizing handwritten Bengali digits is proposed. The main goal is to get a high level of accuracy while using a small number of parameters. The proposed architecture is designed to address the challenges posed by the complex and diverse nature of handwritten numerals in Bengali script, while also being computationally efficient with only 1.08 million trainable parameters. The performance of the model was evaluated by conducting experiments on two commonly used benchmark datasets of handwritten numerals in Bengali script, CMATERdb-3.1.1 and BanglaLekha-isolated-numerals. Different augmentation techniques were utilized to enhance the diversity and size of the training set, which led to improved robustness and generalization of the model. On the CMATERdb-3.1.1 dataset, the proposed model achieved an accuracy of 99.28%, and on the BanglaLekha-isolated-numerals dataset, it achieved an accuracy of 99.12%, outperforming several state-of-the-art models with comparable or larger numbers of parameters. The results suggest that this task-oriented model can be an efficient and effective solution for the recognition of Bengali handwritten numerals, with potential applications in document analysis, digitization, and text recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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Analytical Modeling and Performance Evaluation of Beam-Based Random Access Protocol for 5G Beamforming Networks

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IEEE Internet of Things Journal 2023年第24期10卷 22246-22255页

作者： Lin, Ting-An Cheng, Ray-Guang National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106335 Taiwan

Access parameters, user equipment (UE) density, system load, and channel conditions significantly impact the performance of random-access (RA) protocols, influencing network capacity, latency, and robustness. In 3GPP standard, the prach-ConfigurationIndex access parameter is used to designate reserved RA channel occasions (ROs) for each beam, enabling UEs to derive beam mapping patterns and select desired beams through a beam-based RA protocol. This article addresses the challenge of bursty arrivals from massive Internet of Things (IoT) devices with sporadic traffic patterns in 5G beamforming networks. We propose an analytical model to evaluate the performance of the beam-based RA protocol for any beam mapping pattern. Extensive computer simulations are conducted to validate the accuracy and effectiveness of our model across various scenarios. The results demonstrate that our model provides valuable insights into the behavior and performance of the beam-based RA protocol and offers a reliable tool to evaluate and optimize beam mapping patterns in practical 5G network deployments. © 2014 IEEE.

关键词： Analytical models

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