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A Review on the Applications of Machine Learning and Deep Learning Techniques for Skin Cancer Detection 4

A Review on the Applications of Machine Learning and Deep Le...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine Learning (ML) and Deep Learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 ieee.

关键词： Adversarial machine learning

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A Comprehensive Survey on Enhancing Digital Twin Security systems with Explainable AI Techniques 3

A Comprehensive Survey on Enhancing Digital Twin Security Sy...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Lalithadevi, B. Krishnaveni, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

Digital Twin (DT) technology has emerged as a cornerstone in modern engineering, enabling real-time digital replication of physical assets for monitoring, diagnostics, and optimization. However, as DTs become increasingly integrated into critical infrastructure, their security has become a paramount concern. This paper presents a comprehensive survey on the integration of Explainable Artificial Intelligence (XAI) into Digital Twin security, highlighting the potential of XAI in enhancing the transparency, interpretability, and trustworthiness of AI - driven security mechanisms in DTs. We explore various XAI techniques, including model- agnostic approaches, interpretable neural networks, and rule-based systems, detailing their potential applications in bolstering the security of Digital Twins. Furthermore, we identify several key challenges in implementing XAI for DT security, such as the need for standardized metrics for interpretability, the trade-offs between model complexity and explainability, and the potential for adversarial attacks on explainable models. Through our analysis, we highlight the critical research gaps, including the security of Digital Twin (DT) systems and explores the integration of Explainable AI (XAI) to enhance transparency, interpretability, and trustworthiness. Outcomes include detailed XAI applications for DT security, identification of limitations, and a roadmap for future research. We discuss various XAI techniques, their applications, and the current challenges in implementing XAI for DT security. Our survey identifies key research gaps and offers a roadmap for future research in this emerging field. © 2025 ieee.

关键词： Artificial intelligence

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Digital Evidence Management: Modern Techniques in Digital Forensics 3

Digital Evidence Management: Modern Techniques in Digital Fo...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Venkataramanan, N. Jayalakshmi, V. Anitha, S. Ganesan, R. Jenifer, V. Drbccc Hindu College Department of Computer Applications Pattabiram Chennai600072 India Department of Computer Science and Applications Ramapuram600089 India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai600119 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Department of Computer Science and Applications Ramapuram Campus 600089 India

ISBN: (纸本)9798331509675

This research study reviews the significance of digital forensic analysis in modern criminal investigations, highlighting the way it complements conventional methods of inquiry and the difficulties it encounters in the information era. To solve crimes effectively, investigators must combine digital knowledge with traditional forensic techniques. This will enable them to use evidence from many different types of digital and physical sources. The specific intricacies of digital crime scenes need specialized methods for the proper storage and analysis of digital evidence to identify criminal intent and patterns of conduct. To propose Modern Techniques for Digital Evidence Management [MT-DEM] based Digital Forensics Service. Multidisciplinary teams and cutting-edge technology are becoming necessities in digital forensic investigation as the field adjusts to new threats like data encryption and massive amounts of data. The admissibility of evidence in the legal world depends on digital forensic procedures being transparent and adhering to set criteria. Digital forensic analysis has become an indispensable tool for contemporary investigators with the rise of digital evidence. © 2025 ieee.

关键词： Forensic science

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Edge-Centric IoT Health Monitoring: Optimizing Real-Time Responsiveness, data Privacy, and Energy Efficiency 6

Edge-Centric IoT Health Monitoring: Optimizing Real-Time Res...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Swathi, B. Veerabomma, Supraja Archana, M. Bhadru, Dharavath Somu, Naga Lakshmi Bhavsingh, M. Dept of CSE G Pulla Reddy Engineering College A.P. Kurnool India Department of ECE G Pullaiah College of Engineering and Technology A.P. Kurnool India Department of Computer Science and Engineering CVR College of Engineering Vasthunagar Hyderabad India CSE Department NarSimha Reddy Engineering college Telangana Maisammaguda India Shri Jagdishprasad Jhabarmal Tibrewala University Rajasthan Jhunjhunu India Department of CSE Ashoka Women’s Engineering College Andhra Pradesh Kurnool India

ISBN: (纸本)9798331522667

The innovation of IoT and edge computing has significantly advanced real-time health monitoring systems by providing timely data processing, improved responsiveness, and enhanced data privacy. This study introduces the Edge-Centric IoT Health Monitoring System (EC-HMS), which overcomes limitations observed in cloud-based and hybrid models, including high latency, data privacy vulnerabilities, and excessive energy consumption. EC-HMS leverages localized edge processing to reduce latency, safeguard sensitive data, and optimize energy efficiency, thereby enabling real-time health interventions. The proposed architecture integrates data acquisition, filtering, and anomaly detection at the edge layer, facilitating immediate decision-making and secure data transmission. Experimental evaluations highlight that EC-HMS achieves a 50 ms average latency, consumes 40% less energy compared to cloud-based systems, and ensures robust privacy through localized data handling. This research contributes to the advancement of scalable, responsive, and secure healthcare monitoring frameworks suitable for remote and resource-constrained environments. Future enhancements aim to incorporate predictive analytics and synchronization mechanisms to improve performance and adaptability further. © 2025 ieee.

关键词： Electronic health record

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Perceptual Learning Technology Based on Computer Vision and Machine Learning Algorithms 14

Perceptual Learning Technology Based on Computer Vision and ...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Wang, Tianjian School of Computer Science and Technology Wuhan Qingchuan University Hubei Wuhan430204 China

ISBN: (纸本)9798331531935

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security. © 2025 ieee.

关键词： Image segmentation

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data Protection in Enhanced EXplainable Digital Twins: Insights from Hybrid AI 2

Data Protection in Enhanced EXplainable Digital Twins: Insig...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Gupta, Prashant K. Mazumdar, Bireshwar Dass Pillai, Subash Nataraja School of Computer Science & Engineering Technology Bennett University Uttar Pradesh Greater Noida India Division of Agricultural Physics ICAR-Indian Agricultural Research Institute Pusa Campus New Delhi110012 India

ISBN: (纸本)9798331530389

Deep Learning (DL) based artificial intelligence (AI) is becoming pervasive in almost all the day to day applications. These DL algorithms require large amounts of data, which can be easily collected nowadays by deploying sensors in the environment of the system under observation. These DL algorithms however, suffer from drawbacks such as being criticised as 'black-boxes', being biased to generating predictions based on past data and ignoring the human expertise. Hence, an architecture of Enhanced eXplainable Digital Twins (EXDTs) was proposed. EXDTs remove 'black-boxness' of DL algorithms by using the XAI approaches. Further, the digital twins (DTs) underlying the EXDTs are constructed through the sensor data and depict the real-time simulatability. Also, to improve the explainability and use the human expertise/experience, EXDTs use the domain experts' qualitative data. EXDTs are a class of Hybrid AI systems, that use the numeric data for underlying DL algorithms and subjective knowledge of the domain expert. This multimodal data in EXDTs opens up new attacks fronts for data manipulation and change the EXDTs' outputs. In this manuscript, we discuss about the data protection in EXDTs, by simulating various types of attacks and suggesting possible ways the system can counter them. We feel our proposed framework is useful for the involved stakeholders in various domains like precision agriculture, healthcare, manufacturing, etc. © 2025 ieee.

关键词： Contrastive Learning

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Improving Accuracy of Object Detection in Autonomous Drones with Convolutional Neural Networks

Improving Accuracy of Object Detection in Autonomous Drones ...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Goyal, Himanshu Rai Shrivastava, Anurag Dixit, Krishna Kant Nagpal, Amandeep Reddy, B. Ravali Kumar, Jaysheel Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India GLA Universit Department of Electrical Engineering Mathura India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India

ISBN: (纸本)9798331512965

Increasing use of autonomous drones in such areas as agriculture, disaster response and surveillance means that an effective and precise method of object recognition is becoming more important. In this study, we use Convolutional Neural Networks (CNNs) to bring additional precision to autonomous drone object detecting systems. While conventional machine learning approaches fail miserably in having complicated contexts and real time processing, CNN have sucessfully handles visual identification task. Based on state of the art methods being data augmentation and transfer learning, plus being real time with data augmentation of the raw data on the edge processor, this research introduces a convolutional neural network (CNN) model which has been fine-tuned and can be used for drones. The model is trained and tested on a big dataset of aerial photos acquired by drones. The item classification on this dataset varies from lighting conditions, to weather variables, etc. A considerable increase in the object detection accuracy is found from experimental findings in reducing false positives and improving resilience in dynamic contexts. Furthermore, the suggested model allows for detection with little latency, making it a good model for drones that must be deployed in real-time. Not only does this work contribute to bolstering rapidly growing autonomous drone navigation, it has great promise in environmental monitoring, SAR, and precision agriculture, and other applications. The components of the models are being researched on how scalable they are and how do they scale up on hardware that has limited resources. © 2025 ieee.

关键词： Convolutional neural networks

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Enhancing Real-Time IoT Applications in Cloud Communication Networks Through Adaptive Intelligent Routing and Secure data Transmission 2

Enhancing Real-Time IoT Applications in Cloud Communication ...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Swathi, B. Ramesh, B. Latha, Kambala Madhu Bhavsingh, M. Padma, M. Manjunatha, N. G Pulla Reddy Engineering College Department of CSE Andhra Pradesh Kurnool India CVR College of Engineering Department of ECE Hyderabad India Avanthi's Research and Technological academy Andhra Pradesh India Ashoka Women's Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Lecturer in Computer Applications Government Degree College Srisailam Project Andhra Pradesh Nandyala India Shri Jagdishprasad Jhabarmal Tibrewala University Department of CSE Rajasthan India

ISBN: (纸本)9798331505745

The growing Internet of Things network with cloud technology enables real-time solutions in healthcare, autonomous systems, and industrial automation. These systems face challenges with response time, shifting network layouts, power conservation, and data security threats. RPL and traditional routing protocols struggle to adapt to complex networks, while machine learning-based and EERP approaches solve some issues but leave others unresolved. This study presents an AI routing method that enhances live data transfer between IoT systems and cloud platforms. The algorithm tracks network shifts to make fast routing decisions, reducing time delays and conserving power. This study implement basic encryption, device confirmation, and abnormal event recognition to protect data transfers. Simulations compared the technique's performance to RPL and EERP approaches, measuring latency, data delivery rate, energy usage, network speed, and security capabilities. Testing shows a 25-50% improvement in latency and 10-20% better PDR results. Energy utilization decreases by 30-50%, while throughput remains superior. Security features demonstrate reduced encryption overhead with improved anomaly detection and authentication. This research addresses IoT-cloud communication network challenges through an adaptable system that conserves power, protects data, and enhances reliability for time-sensitive applications. © 2025 ieee.

关键词： Internet of things

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Knowledge Graphs and Neural Networks in Recommendation systems: A Comprehensive Survey and Future Directions 3

Knowledge Graphs and Neural Networks in Recommendation Syste...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jetti, Sharmila Rani Krishna Prasad, M.H.M. Department of Computer Applications GDC Gajapatinagaram India Department of Computer Science UCEK JNTU Kakinada India

ISBN: (纸本)9798331527549

Recommendation systems are crucial components of modern digital platforms, enhancing user experience and driving engagement by providing personalized suggestions. Traditional recommendation models, such as collaborative filtering and content-based filtering, have been widely adopted but face significant challenges, including the cold-start problem, data sparsity, and limited interpretability. Recent advancements have explored the use of knowledge graphs (KGs) and neural networks to address these limitations. Knowledge graphs provide a rich semantic framework for representing user and item relationships, thereby enhancing the recommendation quality by uncovering latent connections. Neural networks, particularly deep learning models, have demonstrated their capability to learn complex patterns from large datasets, resulting in improved recommendation accuracy and personalization. This survey provides a comprehensive overview of the integration of knowledge graphs and neural networks in recommendation systems. It reviews the evolution of recommendation models, from traditional methods to more sophisticated approaches that leverage KGs and neural architectures, such as Graph Neural Networks (GNNs) and Neural Collaborative Filtering (NCF). The paper compares these models based on scalability, accuracy, interpretability, and computational complexity. It identifies key research gaps, including scalability issues, dynamic adaptation of KGs, and privacy concerns, and outlines future research directions to address these challenges. By exploring novel hybrid approaches that combine the strengths of knowledge graphs and neural networks, this survey aims to advance the development of more robust, explainable, and scalable recommendation systems. © 2025 ieee.

关键词： Knowledge graph

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Segmenting the Brain Tumor using Deep Learning Techniques 3

Segmenting the Brain Tumor using Deep Learning Techniques

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Pranitha, Ch. Lakshmi Sumathi, R. Harshitha, V. Kavya, N. Soni, A. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331509675

This study presents a U-Net architecture with EfficientNet-B7 as the feature extraction backbone is used to propose a robust brain tumor segmentation method. Using cutting-edge deep learning techniques, the suggested model precisely segments MRI images with an emphasis on identifying and defining tumor anomalies. A thorough preprocessing pipeline that handles missing values, augments data with albumentations, and normalizes data guarantees the consistency and resilience of the incoming data. The segmentation procedure uses a hybrid loss function that combines Soft Dice Loss and Binary Cross-Entropy (BCE), improving the model's ability to strike a compromise between segmentation consistency and pixel-level precision. To ensure efficiency and generalizability, the model training is optimized using the Adam optimizer in conjunction with a learning rate scheduler. Additionally, an early halting mechanism is incorporated to minimize overfitting. Using a standardized dataset, performance evaluation is carried out with 70%, 15%, and 15% splits for training, validation, and testing, respectively. To evaluate the quality of segmentation, important metrics are employed, such as the Dice Coefficient and the Intersection over Union (IoU). The accuracy of the model in identifying tumor boundaries is illustrated by visualizations of the anticipated segmentation results. The potential of combining U-Net with cutting-edge feature extractors such as EfficientNet-B7 to produce dependable, effective, and clinically significant segmentation results in medical imaging is demonstrated by this approach. © 2025 ieee.

关键词： Deep learning

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