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International Conference on Next Generation Systems and Networks, BITS EEE CON 2022

作者： Suganya, K.S. Nedunchezhian, R. Department of Computer Science and Engineering KCG College of Technology Chennai India Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9789819904822

In recent years, blockchain technology is found to be the most significant discovery in this digital era, after the discovery of the Internet and Cloud Computing. Blockchain is a simple, distributed public ledger that contains all the user's transaction details in a block. The global copy of the block is then shared among all its peer-to-peer network users after validation by the blockchain miners. Once a block is validated and accepted, it cannot be altered by any users making it a trust-free transaction. It also resolves the problem of double-spending by using traditional cryptographic methods. Since the advent of Bitcoin, blockchain is the backbone for all its transactions. But in recent years, it has found its roots and uses in many fields like smart contracts, smart city management, healthcare, etc. Identity management against digital identity theft had become a major concern among financial and other organizations. To solve this digital identity theft, blockchain technology can be employed with existing identity management systems which maintain a distributed public ledger containing details of an individual's identity containing information such as digital birth certificates, citizenship number, bank details, voter details, and driving license in the form of blocks verified on the blockchain becomes time-stamped, unforgeable and publicly visible for any legitimate users. The main challenge in using blockchain technology for preventing digital identity theft is ensuring the pseudo-anonymity and privacy of the users. This survey paper will exert to study the blockchain concepts, consensus protocols, and various blockchain-based digital identity management systems with their research scope. This paper also discusses the role of blockchain in COVID-19 pandemic management through self-sovereign identity and supply chain management. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain technology

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computer Modeling in engineering & sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Exploring Innovation: Artificial Intelligence (AI) in Pharmacy 7

Exploring Innovation: Artificial Intelligence (AI) in Pharma...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Raymond, David Mankar, Divyani Gudadhe, Amit Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Basic Science And Humanities Maharashtra Wardha India

ISBN: (纸本)9798350359299

Artificial Intelligence (AI) has emerged as a disruptive force in the pharmaceutical industry, driving unprecedented breakthroughs across various domains. This article provides a comprehensive overview of the cutting-edge applications of AI in pharmacy, spanning medication safety, clinical decision support, personalized medicine, and drug discovery. AI is revolutionizing drug development processes, enabling precision pharmacy techniques, and enhancing clinical decision-making through Machine Learning, Deep Learning, and Natural Language Processing. Despite the potential for transformation, challenges such as moral dilemmas and legal restrictions persist. Nevertheless, the integration of AI will revolutionize pharmacy practice, leading to improved patient outcome and ushering in a new era of healthcare. © 2024 IEEE.

关键词： Deep 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 Neural Network Model for Improved DDoS Attack Detection in Cloud Environments 5

Deep Neural Network Model for Improved DDoS Attack Detection...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Verma, Rashmi Jailia, Manisha Kumar, Munish Kaliraman, Bhawna Department of Computer Science and Engineering Banasthali Vidyapith Rajasthan Tonk India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

ISBN: (纸本)9798350361155

As the prevalence of Distributed Denial of Service (DDoS) attacks continues to escalate, safeguarding cloud environments against these threats becomes paramount. This paper introduces a Deep Neural Network (DNN) model designed to enhance the accuracy and efficiency of DDoS attack detection in cloud environments. Leveraging the inherent capabilities of deep learning, the proposed model exhibits improved performance on the widely recognized NSL-KDD dataset. The research findings demonstrate a substantial increase in accuracy, underscoring the efficacy of the DNN model in fortifying the security posture of cloud infrastructures. The escalating frequency and sophistication of Distributed Denial of Service (DDoS) attacks pose a substantial threat to the security of cloud environments. In response to this pressing concern, this paper introduces a Deep Neural Network (DNN) model engineered to significantly enhance the accuracy and efficiency of DDoS attack detection in cloud infrastructures. By harnessing the inherent capabilities of deep learning, the proposed model represents a breakthrough in fortifying the security posture of cloud systems. This research employs the widely recognized NSL-KDD dataset, a comprehensive resource for Intrusion Detection System (IDS) evaluation, to evaluate the model's performance. The proposed DNN model transcends conventional methods by autonomously learning intricate patterns within network traffic data, adapting to the evolving landscape of DDoS attacks. The literature survey delves into the vulnerabilities associated with DDoS attacks in cloud environments, emphasizing the need for innovative detection mechanisms. Previous research has underscored the effectiveness of deep learning, particularly DNNs, in addressing complex cybersecurity challenges, positioning them as ideal candidates for enhancing threat detection capabilities. © 2024 IEEE.

关键词： Denial-of-service attack

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A Heuristic Approach to Identify Influential Node in Complex Social Networks 14

A Heuristic Approach to Identify Influential Node in Complex...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Dutta, Sangita Chakraborty, Susanta Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Howrah India

ISBN: (纸本)9798350335095

Multiple features of the complex network are exhibited among the nodes in time-varying social networks. Community detection has numerous approaches, but the majority of methods are restricted to disjoint community situations. Solutions to disjoint community situations do not reflect well on real-world social networks. An innovative heuristic method has been proposed to recognize community structure in real-world networks and to identify the most influential node within each community. The proposed concept on a recognized community network has been tested in order to validate it. This work introduced one new network metric, the Dependency factor which computes the influence of a node. Our results are compared with other existing approaches using real-life datasets. We have compared the dependency factor with other network centralities. The computational case study is done over a well-known Bengali novel, namely Chaar Adhyay written by Nobel laureate Rabindranath Tagore. The analytical results are more accurate than the state-of-the-art approaches. © 2023 IEEE.

关键词： Heuristic methods

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of computer science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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Algae species classification based on few-shot learning: A case study of Macau freshwater 9

Algae species classification based on few-shot learning: A c...

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9th International Conference on Communication and Information Processing, ICCIP 2023

作者： Wang, Binbin Yuan, Anran Zou, Hailin Chen, Zijie Li, Jianqing School of Computer Science and Engineering Macau University of Science and Technology Taipa China School of Computer Science and Engineering Macau University of Science and Technology Zhuhai MUST Science & Technology Research Institute Taipa China

ISBN: (纸本)9798400708909

Harmful Algal Blooms (HAB), a significant environmental concern on a global scale, are damaging to ecosystem functions and pose challenges to environmental management. The key to HAB management is to classify the local representative species. However, the widespread adoption of automated monitoring devices such as the Imaging FlowCytobot (IFCB) is cost-prohibitive and often cannot be efficiently utilized. An algae classification algorithm based on transfer learning designed to classify local algal species with limited low-cost microscope-based algae images is developed to offer a cost-effective and practical solution for HAB management. The model is tested on the Macau representative freshwater algae dataset. The training process of the transfer learning model is based on the automatically captured open-source dataset and IFCB-based Hong Kong tropical algal dataset. The results demonstrated the exceptional performance of our method, achieving up to 100% accuracy in the classification of single algal species. Furthermore, when compared with commonly used deep learning models in experimental comparisons, our method exhibited superior performance. We also conducted ablation experiments on transfer learning, which validated the effectiveness of our transfer learning approach. © 2023 Copyright held by the owner/author(s)

关键词： Algae

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment 4

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Dandotiya, Monika Singh, Anupam Agarwal, Nishi Jaheda, Shaik Gulati, Samridhi Poornima University Department of Computer Science & Engineering Jaipur India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Dev Bhoomi Uttrakhand University Department of Computer Science and Engineering Dehradun India Cvr College of Engineering Department of Information Technology Hyderabad India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350364729

Liver cancer is still one of the top causes of worldwide mortality from cancer, which highlights the demand for earlier diagnostic tools aimed at improved treatment outcomes. Although these traditional diagnostic practices are helpful, they may take time and also be subject to human error. The present study aims to fill the gap in existing diagnostic tools for liver cancer by evaluating prostate deep learning models (VGG 16, VGG 19 and ResNet 50) on CT scan images. This problem statement is related to improving the accuracy and reliability of liver cancer detection using powerful computational techniques. We processed a dataset of 1020 CT scan images by resizing, then normalizing the data, and performing an augmentation and some noise reduction. The performance of each model was evaluated in terms of precision, recall and F1 score using Dice Similarity Coefficient to identify the liver tumours. VGG 19 was the top performer in terms of accuracy and another score, followed by VGG 16 and ResNet 50. The use of such models in clinical settings can significantly improve diagnostic accuracy and, ultimately, diagnosis time, which will lead to better patient outcomes. The study ultimately underscores the promise of deep learning models (specifically VGG 19) as an essential resource in medical imaging by developing new and prospective methods for computerized liver cancer diagnosis that will help improve healthcare. © 2024 IEEE.

关键词： Diseases

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VSNet: classification of pulmonary nodules in 3D using vision transformer and sequence spatial attention mechanism

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Multimedia Tools and Applications 2025年第14期84卷 13885-13903页

作者： Tang, Dongfang Xiao, Ting Yang, Fan Zhang, Conghao Wang, Zhe Gao, Wen Department of Thoracic Surgery Huadong Hospital Affiliated to Fudan University Shanghai200040 China Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China

Accurate classification of benign and malignant nodules in Computed Tomography (CT) scans is crucial for the early detection of lung cancer and computer-Aided Diagnosis (CAD) systems. Despite significant advancements, challenges such as the interpretability of the reasoning process and the lack of fine-grained representations persist. To address these challenges, we propose a 3D VSNet. Our approach incorporates a Sequence Spatial Attention Module (SSAM) that automatically locates the sequence of the encoder’s output and the receptive field region required, achieving the acquisition of key features of pulmonary nodules. We leverage the characteristics of shallow and deep features based on 3D Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) to obtain fine-grained representations and improve nodule classification. Additionally, we implement a new training strategy using Supervised Contrastive (SC) loss and Proxy-Anchor (PA) loss to optimize the embedding feature of similar samples and the direct cosine distance, and enhance the convergence speed of tuple sampling. Our experiments on the LIDC-IDRI dataset demonstrate the effectiveness of our proposed technique, achieving an accuracy of 90.28% and a precision of 91.63%. Furthermore, we conduct ablation experiments to analyze the contribution and influence of each component of our method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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