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检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

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Skin Cancer Detection Using A Multi-Scale AI Deep Learning Approach 5

Skin Cancer Detection Using A Multi-Scale AI Deep Learning A...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Appavu, Narenthirakumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350356236

Skin cancer occurrences are increasing, and melanoma is the deadliest type of the disease. Preventing the illness from spreading to other regions of the body requires early identification. In this work, an automated technique for identifying skin cancer without the need for physician assistance is presented. The suggested method for detecting melanoma lesions makes use of transfer learning. To reduce lighting effects and noise, the acquired photos are first pre-processed. By creating more training examples, image augmentation - a crucial deep learning technique - is then used to improve the dataset and raise the training process' efficacy. Then, using transfer learning, a Convolutional Neural Network (CNN) is built using pre-learnt weights from the ImageNet dataset. The network is then adjusted to prioritise the detection of melanoma and differentiate it from benign tumours. Each of the three main phases in the method's processing of image data focusses on the central area of the picture where the suspicious lesion is located. The results from these processes are aggregated and used to train a fully connected neural network. The suggested solution outperformed other cutting-edge techniques, as seen by the 90.09% accuracy attained in testing on a collection of photos. Through concentrating on the questionable skin region at different phases, the method offers a more precise and trustworthy diagnosis. © 2025 IEEE.

关键词： Convolutional neural networks

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Integration of Blockchain and Fog computing—A Review 5th

Integration of Blockchain and Fog Computing—A Review

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5th international conference on advances in Information Communication Technology and computing, AICTC 2024

作者： Kaur, Nirbeh Kumar, Shubham Kumar, Ritik Ranjana, Khushi Anand, Kumar Abhishek Gupta, Shailendra Kumar Department of Computer Science and Engineering Chandigarh University Punjab Mohali India Department of Electrical Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9789819761050

"Integration of blockchain and fog computing" involves the two different concepts, i.e. blockchain technology and fog computing which has something in common—the decentralised system. The rapid advancement of technologies all over the world has put some threats to the data and information along with its development. The fog computing concept was put forward to enhance cloud computing applications in handling the IoT data. Fog computing, on the other hand, faces a number of difficulties, including storage, privacy, and security. Integration of blockchain with fog computing is the main approach which can help in overcoming these difficulties. Several researches are being done for integrating blockchain with fog computing in the field of health care, smart home, smart cities, IoT applications, drones network, mobile devices, transportation, financial, supply chain management, and many more. This research paper is a review study of relevant papers published on this topic already and gives an insight of the applications of integration of blockchain and fog computing. Our review paper has a systematic study of existing literature on this topic, and it provides an excellent overview of all the concepts that will play a crucial role for the researchers in their future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Advanced Deep Learning Framework for Diagnosing Autism Spectrum Disorder Through Facial Expression Analysis 5

Advanced Deep Learning Framework for Diagnosing Autism Spect...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Balasubramani, Jagadesh Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350356236

Autism Spectrum Disorder is a neurodevelopmental disorder that presents with persistent deficits in social communication and social or emotional reciprocity. Timely intervention may result from an early and precise diagnosis of autism spectrum disorder (ASD). This study proposes a paradigm that uses deep learning systems to analyze facial expressions and emotions to classify children with autism spectrum disorder. The proposed framework utilizes a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks (CNN-BiLSTM) model to learn and perform classification tasks, To enhance image quality, input data undergoes pre-processing techniques, including histogram equalization, which improves contrast, and adaptive median filtering, which reduces noise while preserving essential details. Model optimization is a self-adaptive Black-Winged kite optimization method for hyperparameter tuning, ensuring that the model's parameters are optimal for improved classification accuracy. Performance measurement results such as Accuracy, recall, precision, specificity, and confusion matrix shown from evaluating the framework in ASD datasets obtained from the Kaggle repository are implemented in Python. This approach enhances the speed and precision of Autism Spectrum Disorder diagnosis accordingly and gives a very powerful instrument for real-time clinical application. © 2025 IEEE.

关键词： Image enhancement

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Multi-Camera Trajectory Based Vehicle Re-Identification for Robust Traffic Monitoring 5

Multi-Camera Trajectory Based Vehicle Re-Identification for ...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Athilakshmi, R. Prasanna, Emandi Venkata Sanjeevi Sai Yasir, Syed Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (纸本)9798350356236

Vehicle Re-Identification (ReID) is the challenging task of identifying the same vehicle across multiple cameras in urban environments. In this paper, we propose a novel MultiCam TrajectoryID model that leverages trajectory-based features, addressing the limitations of single-image-based ReID methods. The system combines feature extraction methods like SIFT with the Kalman filter and You Only Look Once (YOLO) algorithms for trajectory prediction to guarantee accurate and effective tracking of objects across several camera views. This MultiCam TrajectoryID approach enhances traditional single-image vehicle ReID by focusing on the continuity of object movement, improving the robustness of identification in complex, real-world traffic environments. We evaluate the performance of our model on the challenging VeRi-776 dataset and compare it with state-of-the-art methods. The proposed model achieves a mean average Precision (maP) of 86% on VeRi-776 data, outperforming recent approaches such as Multi-Branch Enhanced Discriminative Network and Multi-Fine Grained Network models. This significant improvement demonstrates the effectiveness of trajectory-based re-identification in multi-camera setups and its contribution to vehicle ReID and real-time surveillance systems. © 2025 IEEE.

关键词： Vehicle detection

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AI-Driven Green computing for 6G Network: Improve User Experience and Security 1

AI-Driven Green Computing for 6G Network: Improve User Exper...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Yadav, Rakesh Kumar Harinaiha, Adarsha Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar India

ISBN: (纸本)9798331518578

A dynamic Computational Thinking resolution can be likely in a 6G environment by way of Green-IoT and Artificial Intelligence (AI), important parts of computational thinking. This order requires lowering difficult problems to tinier, more controllable parts and recognizing patterns and friendships at intervals in miscellaneous parts. Machine intelligence approaches concentrate on algorithms capable of learning from data, making judgments and predictions, and executing complex tasks beyond explicit programming. To appreciate the far-reaching quantities of data presented by 6G-connected schemes, computational inference and machine intelligence can be used for the Green-IoT. This has the potential to reinforce the computerization of many processes and bring about a smarter and more efficient use of possessions. Computational thinking that combines Green-IoT, AI, and 6G sciences can transform many areas, including education, and reinforce society's lives through the production of brisker, more adept, and more tailor-made uses and services. The research has been started by outlining key limits utilizing Green-IoT-authorized sensor electronics in the framework of 5G and 6G surroundings, as well as Artificial Intelligence, in this place study's review on computational thinking. In the subsequent section, delineate the fundamental structural requirements for a digital thinking paradigm centered on artificial intelligence and the Net of Things, significant aspects of prospective beneficial applications, and evaluate the advantages and disadvantages of each within the context of shared resources. Lastly, several security risks to an AI-based architecture that might compromise the system's ability to provide the computational thinking services that social media platforms need. © 2025 IEEE.

关键词： Economic and social effects

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IoT-Based Transfer Learning and Genetic Algorithm Approaches for Smart Environmental Pollution Detection and Management 5

IoT-Based Transfer Learning and Genetic Algorithm Approaches...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Alauddin, Shafqat Sree Vidhya, S. Pragadeeswaran, S. Sowjanya, P. Bhokare, Rajashree Khan, Maaz Allah Shibli National College Department of Chemistry Uttar Pradesh Azamgarh India Erode Sengunthar Engineering College Department of Computer Science and Engineering Tamilnadu Thudupathi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Department of Ece Telangana Dhulapally India Dr. D. Y. Patil Institute of Technology Department of Electrical Engineering Pimpri Maharashtra Pune India Department of Engineering & Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9798350356236

The identification of environmental pollution remains to be a significant issue, as conventional technologies lack scalability and flexibility to dynamic, real-time situations. This study presents an IoT-enabled hybrid model that combines transfer learning and genetic algorithms to overcome these restrictions. IoT networks enable real-time, extensive data acquisition, whereas transfer learning uses pre-trained models for effective feature extraction and domain adaption. Genetic algorithms enhance hyperparameters and sensor configurations, guaranteeing energy economy and optimal model performance. The suggested CNN-LSTM architecture utilising multi-scale attention attains a training accuracy of 95.6% and an F1 score of 0.96, exceeding the performance of conventional Random Forest and CNN models in accuracy, recall, and precision. The comparative study confirms the model's strength and flexibility, illustrating its capacity for scaled, practical applications. This framework creates a dependable and effective system for intelligent environmental pollution control, providing substantial improvements in detection accuracy and operational efficacy compared to current methods. © 2025 IEEE.

关键词： Environmental management

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Smart Sensors and IoT in Mechanical Engineering: Enhancing Monitoring and Control of Industrial Processes 1

Smart Sensors and IoT in Mechanical Engineering: Enhancing M...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Annavarapu, Babu J Hareesha, N.G. Kacheru, Goutham Mohammad, Ashif Chin, Jacky Ghule, Gauri KLEF Deemed to be University Department of Management Andhra Pradesh Guntur India Dayananda Sagar College of Engineering Department of Aeronautical Engineering Bengaluru India Wilmington University Department of IST United States University of Dhaka Department of Institute of Energy Bangladesh Mercu Buana University Department of Industrial Engineering West Jakarta Indonesia Department of E and TC VIIT Pune India

ISBN: (纸本)9798331518578

Unmanned aerial vehicles (UAVs) are currently regarded as among the most effective remote sensing methods for collecting data across wide regions. Along with measuring output and aiding in decision-making, they are now employed in the industrial domain as sensing equipment for proactively resolving or avoiding numerous problems. UAVs offer an economical, functional, and reliable technical system for event monitoring and data collecting. Cloud computation has proven to be an essential instrument for addressing processing needs, and the Internet of Things (IoT) transmits industrial data from platforms that monitor and manage surroundings to data processing platforms. The IoTs port connects many items to the internet in fog computation. It supports many communication standards and can function as a joint portal for various systems. Much work has gone into creating UAVs and platforms with several UAVs. This work presents a smart IoT monitoring and control platform depending on a UAV. It utilizes fog computation as an interface between IoT tiers and cloud computation activities. Its ability to seamlessly connect UAVs to industrial control platforms via IoT port platforms and process and analyze UAV shots in real-time and in a structured manner in the cloud is what makes it distinctive. The outcomes show unequivocally that the suggested structure is feasible in offering a dependable and effective UAV distant control platform to enhance product grade and minimize waste. Hence this study investigated the communication delay in several IoT ports between the various IoT levels. © 2025 IEEE.

关键词： Fog computing

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 international conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP international Federation for Information Processing 2025.

关键词： COVID-19

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Enhanced Security Through Honeywords and Video CAPTCHA: An Innovative Authentication and Tracking Mechanism 5

Enhanced Security Through Honeywords and Video CAPTCHA: An I...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Gurale, Madhuri Borate, Komal Sangitrao, Rachana R. Tiwari, Garima D. Y. Patil College of Engineering Information Technology Department Akurdi Pune412101 India D. Y. Patil College of Engineering Artifical Intelligency and Data Science Department Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Instrumentation and Control Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Electronics & Communication Akurdi Pune412101 India

ISBN: (纸本)9798350356236

In recent years, activities across countries have increasingly been carried out digitally, with information being shared over networks to boost speed and efficiency. However, this digital transformation poses security threats. Cybercrime has consistently evolved, necessitating improved network security measures to combat digital scams. To maintain security, users are assigned unique identification passwords, which they are instructed to keep secret. Despite this, attackers often manage to steal passwords. To counter these threats, we propose using honeywords, which are decoy passwords created from the existing user password. If an attacker inputs a honeyword, an alert is activated for the administrator. Moreover, we implement IP and location monitoring for users and introduce a approach known as the video click-based CAPTCHA system. This scheme authenticates users by distinguishing between humans and bots, addressing issues in traditional graphic password CAPTCHA schemes. This architecture significantly enhances protection and security of data and applications over online networks. © 2025 IEEE.

关键词： Malware

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Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Learning Techniques 5

Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Lea...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Shams, Karib Saha, Ratul Rodela, Mahmuda Islam Simran, Sanjana Rashid Khan, Musharrat East West University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798350356236

Tuberculosis (TB) is a significant global health challenge, necessitating precise and efficient diagnostic tools. This work investigates the effectiveness of various deep learning models such as EfficientNetB3, DenseNet121, VGG19, DenseNet201, and ResNet50 in detecting TB from chest X-ray images. Utilizing the TB Chest X-ray database, we evaluated pre-trained models, developed custom architectures, and employed the k-fold validation method. The EfficientNetB3, with a customized design, demonstrated superior performance, while the DenseNet201 excelled in the k-fold validation setup. Our findings highlight the potential of the EfficientNetB3 and DenseNet201 in advancing TB diagnostics, offering significant improvements in speed and accuracy. We got 100% accuracy in EfficientnetB3. This study not only contributes to the growing field of AI in medical imaging but also underscores the ethical considerations required in deploying AI for healthcare, ensuring patient privacy and equitable algorithmic decisions. The outcomes of this study hold promise for improving real-time healthcare through more effective TB detection and patient care. © 2025 IEEE.

关键词： Disease control

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