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2023 1st DMIHER International Conference on artificial intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Langote, Meher Chandankhade, Arpit Waghade, Munabber Zade, Shizhan Lokhande, Priyanshu Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Sawangi India

ISBN: (纸本)9798350338423

The Internet of Things (IoT) has acquired increasing significance in agriculture and food production in recent years, expanding its reach beyond smart city applications. In this comprehensive study, we present a pioneering, exceptionally scalable IoT system specifically tailored for agricultural purposes. This innovative system leverages the Low-Power Wide-Area Network (LoRaWAN) for long-distance, energy-efficient data transmission from sensor nodes to cloud-based services. This IoT solution is underpinned by a highly extensible cloud computing framework that effectively harnesses data streams for in-depth analytics. In the study, we introduce an innovative, highly scalable Internet of Things agricultural system. For long-distance and low-power data transfer from the sensor nodes to the cloud services, this system is built on the LoRaWAn network. Our highly scalable cloud computing solution makes use of data streams for analytics. In this case study, we display some early findings for a grape vineyard. © 2023 IEEE.

关键词： Internet of things

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AI-Enhanced Imaging Techniques for Understanding Alzheimer's Progression 2

AI-Enhanced Imaging Techniques for Understanding Alzheimer's...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Chaudhari, Aarti Saratkar, Saniya Thute, Trupti Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Alzheimer's disease (AD) is a fatal neuronal disorder which occurs mainly in the elderly and causes a gradual dementia with or without memory loss, functional disability and death. AD is the most prevalent form of dementia, and the condition is positively affiliated with amyloid-beta plaques, tau protein tangles, neuronal ischemic losses, and brain atrophy. Even after studying for a long, the cause(s) of AD has not been well established, though it is well understood that age, genetics (specifically APOE ?4 gene), and lifestyle are some of the leading causes. At the moment there is no cure and management involves the alleviation of symptoms and the prevention of disease progression. The key for better patient management is the early-stage triage as this would increase the chance of getting proper care, fulfilling criteria for clinical trials, and receive better therapeutic management. For a long time now, an early diagnosis has been emphasized due to the improved prognosis, but little has been done in that area instead a lot of efforts are directed at treatment. MRI, PET, and fMRI consisting of imaging approaches that enables the identification of brain changes and progression of the disease. Healthcare & medicine;artificial intelligence (AI) is changing the way Alzheimer's is diagnosed and treated. In fact, classifiers such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformer models can investigate imaging data and distinguish the features of early stage, progression, and survive rate, so as to assist with medical treatment planning. The general strength of AI to analyses and manage large amounts of information provides new chances for enhancing clinical practice and investigation in Alzheimer's disease. © 2025 IEEE.

关键词： Neurodegenerative diseases

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for Image Recognition 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 Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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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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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Analysis of GAN for Melanoma Skin CancerClassification with Dermatologist Recommendation

Analysis of GAN for Melanoma Skin CancerClassification with ...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational intelligence, RAEEUCCI 2024

作者： Saranya, N. Jowin Alfred, C. Rr, Rishikesh Gilbert Idayan, I. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354539

Melanoma is a type of skin cancer that poses a significant health threat globally, necessitating effective early detection methods. Melanoma arises from the pigment-producing cells(melanocytes) and is known for its higher metastatic potential. Designing a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection is challenging due to the appearance of the nevus. This study investigates the application of a GAN for classification of melanoma. The GAN architecture is employed to learn and generate synthetic melanoma images, enhancing the diversity and richness of the training dataset. We mainly focus on evaluating performance of GAN-enhanced model through comprehensive metrics like specificity, accuracy, the receiver operating characteristic curve (ROC) and area under the curve (AUC). This study sheds light on utility of synthetic data in clinical image analysis and provides insights into the strengths and limitations of utilizing GANs for melanoma skin cancer classification. We also added a dermatologist recommendation feature to our model. It uses computer programs to understand what youneed and then matches you with the best doctor available nearby. This system helps you get the care you need faster and easier, making sure you see a doctor who's a good fit for you and your health needs. This system matches patients with suitable doctors. This matching involves considering factors like doctor specialty and location. © 2024 IEEE.

关键词： Dermatology

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SAR Ship Detection Based on Multi-Scale Feature Cross Fusion

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Progress in Electromagnetics Research B 2025年 110卷 149-161页

作者： Ren, Xiaozhen Zhou, Peiyuan Liu, Gang School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

Synthetic aperture radar (SAR) ship detection plays a significant role in ocean monitoring. However, the current SAR ship detection methods face limitations in detecting small and dense ships. To address these issues, a novel SAR ship detection method based on multi-scale feature cross-fusion (MFCNet) is proposed in this paper. In the proposed model, a feature extraction network with a spatial fusion attention mechanism (FESNet) is designed to improve the capability of the backbone network in feature extraction. A multiintersection spatial pyramid pooling (MISPP) module is proposed to expand the receptive field and enhance the semantic information. Furthermore, a feature cross-fusion network (FCFNet) is designed to comprehensively integrate features of different scales for enhancing SAR ship detection performance. Experimental results demonstrate that the proposed model achieves high detection performance on the SSDD and HRSID datasets, providing more reliable technical support for ship detection in maritime environments. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Synthetic aperture radar

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Navigating the Cloud: Enhancing Healthcare through Opportunities and Challenges of Cloud Computing 8

Navigating the Cloud: Enhancing Healthcare through Opportuni...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Saratkar, Saniya Langote, Meher Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798350376425

The availability of computing resources and services through the Internet is a revolutionary approach to delivering computer resources. It is considered by many managers and experts as capable of making changes in the state and quality of health care services research, and health information technology. technology has been described as a revolution in the health sector in which cloud computing is a major development that offers hitherto opportunities for creativity, efficiency, and cooperation. In this article, the author exposes the possible changes brought by cloud computing in the context of healthcare services. It analyses the several ways cloud computing could potentially revolutionize the delivery of healthcare services concerning enhanced data storage and processing, modularity, and open access. Cloud computing is a modern approach to delivering resources and services that focus on computing. It is thought that many managers and experts have the potential to enhance healthcare services and research and change health information technology. Cloud computing is among the most revolutionary technical innovations applied in the healthcare sector, creating unprecedented creativity, value, and synergism. The following article aims to investigate the enabling role of cloud computing in the UK healthcare services industry. They look at the different approaches that cloud computing is likely to deliver for the provision of healthcare in terms of accessibility, scalability, data management, and even analysis. © 2024 IEEE.

关键词： Electronic health record

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Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction

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Frontiers of Computer science 2025年第2期19卷 137-138页

作者： Enchang ZHU Zhengtao YU Yuxin HUANG Shengxiang GAO Yantuan XIAN Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing methods are still not effective due to the following two issues:(1)there are difficulties in contextual modeling of long text,which requires modeling and understanding coherence and connections across sentences and paragraphs;(2)there usually ignore the explicit dependency relationships between event elements displayed in long *** this end,we propose a novel graph-augmented approach for document-level event role filler extraction,named element relational graph-augmented multi-granularity contextualized encoder(ERGM),whose main idea is to effectively enhance the model's capabilities in capturing deep semantic information of events in long texts and modeling dependency relationships among event elements by incorporating the Event elements relational ***,this method first constructs the structural graph by extracting elements from the source document.

关键词： contextual spite semantic

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