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An In-Depth Review of Machine Learning, Blockchain, and Deep Learning Models for Intelligent Security and Resilience Enhancement in Smart Cities

An In-Depth Review of Machine Learning, Blockchain, and Deep...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Lanjewar, Umesh Khekare, Ganesh Wahi, Amitabh Bhagwant University Department of Computer Science & Engineering Ajmer India School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331517953

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract) © 2024 IEEE.

关键词： Adversarial machine learning

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Lightweight Deep Learning Models for Robust Hand Gesture Recognition 5th

Lightweight Deep Learning Models for Robust Hand Gesture Rec...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Nisha Sonu Narayan, Satya Gajrani, Jyoti Department of Computer Science and Engineering Engineering College Ajmer Rajasthan Ajmer India

ISBN: (纸本)9789819761050

Hand gesture recognition is a burgeoning area of study with wide-ranging applications, including human–computer interaction, sign language translation, and virtual reality control. In this work, we introduce an innovative lightweight DL model designed for robust HGR and provide an in-depth analysis of its classification performance. We meticulously evaluate key performance metrics such as precision, detection rate, Jaccard index, and false positive rate and compare these results with existing methodologies. Our proposed model stands out by surpassing the performance of current models. It exhibits higher accuracy rates during both training and validation, a substantially improved detection rate, and an almost perfect Jaccard index. Most impressively, our model achieves a remarkable false positive rate of zero, showcasing its exceptional reliability in distinguishing between positive and negative instances. These findings underscore the effectiveness of our approach in addressing challenges related to overlapping gestures, unbalanced class distributions, and precise HGR. In our conclusion, we identify areas for potential enhancement and further research. To clarify the novelty and research gap of our proposed model, we emphasise its lightweight nature and articulate how it differs from existing methods. This distinction lies in its capacity to deliver accurate and efficient HGR while imposing minimal computational burden, setting it apart from more resource-intensive approaches. In summary, our study and potential to revolutionise practical applications that demand dependable and effective hand gesture recognition systems, bridging existing gaps in the area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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VMFMAX: Improving Virtual Machine Utilization in VNF Placement

VMFMAX: Improving Virtual Machine Utilization in VNF Placeme...

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2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Taghizadeh, Seyedreza Emami, Parnian Taleghani, Hamid Reza Tarmah, Ali Taghizadeh, Seyedehnarjes Shiraz University Department of Computer Science Engineering and Information Technology Shiraz Iran Shiraz University Department of Electrical and Computer Engineering Shiraz Iran NASINE Co. Department of Information Technology Shiraz Iran

ISBN: (纸本)9798350385144

In order to improve the performance of contemporary and prospective technologies grounded in Network Function Virtualization (NFV), it is imperative to concentrate on identifying the most effective strategies for the placement of Virtual Network Functions (VNFs) within NFV frameworks. Consequently, the objective of this study is to present a viable solution for VNF placement. We delve into the significance of factors such as Storage Latency, CPU Model, and Network Packet Dropped, among others, which necessitate careful consideration. These parameters wield substantial influence over Virtual Machines (VMs) and overall VNF placements. Through the judicious selection of suitable VMs for VNFs, our aim is to improve the placements of VNFs, employing our proposed methodology termed VMFMAX. Our findings reveal that VMFMAX has efficient and less time and resource-consuming calculations compared to previous studies. VMFMAX conspicuously impacts the outcomes of the VNF placement process, underscoring its critical role in achieving more precise and accurate calculations. © 2024 IEEE.

关键词： Edge computing

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An Explainable AI for Prostate Cancer Analysis 5th

An Explainable AI for Prostate Cancer Analysis

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Kumar, M. Rakesh Athish, B.S. Devi, A. Chandini Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College TN Chennai India

ISBN: (纸本)9789819771776

Prostate cancer comes under SDG 3—Good Health and Well-Being is a global health concern affecting millions of men, demanding precise analysis for early detection and effective treatment. This project pioneers, innovative techniques for prostate cancer analysis, utilizing AI-driven segmentation and advanced analytical methods to address this pressing concern. Prostate cancer poses a significant worldwide health challenge, potentially metastasizing to neighboring organs and bones, leading to pain and fractures. The primary aim of this project is to enhance the precision and efficiency of prostate cancer analysis for early detection and timely intervention. Approximately one in eight men receive a prostate cancer diagnosis during their lifetime, with a higher prevalence among older individuals and non-Hispanic black men, particularly those aged 65 and older. Awareness levels vary, with 47.3% of survey respondents aware of the condition, primarily through media outlets (21.2%) and healthcare professionals (9%). This project addresses varying prostate cancer rates in distinct regions (peripheral zone: 70–80%, transitional zone: 10–20%, central zone: 5% or less). It employs AI-driven segmentation, image processing, and machine learning for accurate tumor detection, classification, and risk assessment. An innovative aspect is developing an explainable AI system for transparent and expert-empowered analysis. The project uses AI to diagnose prostate cancer accurately and early. It combines AI-driven segmentation, advanced analytical methodologies, refined algorithms, and a user-friendly interface. The aim is to enhance patient care through accurate and interpretable prostate cancer analyses in underserved areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

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Intelligent Automation & Soft Computing 2023年第2期35卷 1873-1887页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the *** fundamental problem with WSN is that it has aﬁnite *** a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data *** cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunﬂower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is *** does not provide better competence and out-put in the ***,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the *** hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used toﬁnd the route among origin CH and *** open cache of nodes,trans-mission power,and proximity are used to improve the CH *** grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle *** GWO method was inspired by this hunting *** proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale *** HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

关键词： Energy efﬁciency power consumption lifespan of the network black hole method ant colony optimisation routing and cluster heads(CHs)

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Daily Life Conversation in Bangla Sign Language Using Machine Learning Technique 6

Daily Life Conversation in Bangla Sign Language Using Machin...

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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Shakil, Mirza Md Amin, Al Anam, Tahmid Hussein, Molla Rashied Alam, Tahira Chowdhury, Jamim Hossain University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh University of Skill Enrichment & Technology Department of Computer Science and Engineering Narayanganj Bangladesh

ISBN: (纸本)9798350396133

Sign language is a way of communicating and interacting for access to information. Currently, deaf and mute people rely heavily on sign language to communicate. It enables the challenged person to interact with a hearing-enabled person to express themselves and participate in all facets of life. In these circumstances, research is needed in the area of sign language recognition as limited work has been conducted. Also, as it varies from region to region and in the context of BangIa sign language, related research is more mandated. The dataset in this study consisted of 9,000 video data. Alongside this, Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) were used to establish a two-way communication system. LSTM is used for long-term dependencies and sequence predictions. To gather video data and extract characteristics from the video frames, MediaPipe and OpenCV were employed. Fine-tuning techniques, such as the number of hidden layers, the density of layers, the number of neurons within each layer, the learning rate, the optimizer, and other parameters were used to determine the optimal accuracy. The results showed that 96% of the categories were accurately represented by scikit-Iearn. This result offers a reference for employing deep learning structures for the detection of BangIa sign language and the development of dependable assistive technology tailored to the necessity of the hearing-impaired community. © 2024 IEEE.

关键词： Gesture recognition

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Advancing Cardiovascular Disease Prediction Using LLMs 2

Advancing Cardiovascular Disease Prediction Using LLMs

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

作者： Zanab, Tazveen Bhavani, K. Shunmugapriya, B. Shenbagaraman, A. Kanthimathi, M. National Engineering College Department of Computer science and engineering Kovilpatti628503 India

ISBN: (纸本)9798331505745

The heart is perhaps one of the most vital organs supporting human life, but it is more at risk in our contemporary world with its rapid pace of life. The lifestyles in modem times and exert an immense burden on cardiovascular health. All these factors have led to a growing rate of cardiovascular diseases (CVDs) across age groups and turned heart disease into a predominant cause of death globally and an emergent public health issue. One of the most urgent challenges in the fight against heart disease is the challenge of obtaining timely diagnosis. Most people only get to know about critical cardiovascular diseases after they suffer from life-threatening incidents like heart attacks or associated complications. The delay is usually a result of the insidious and deceptive presentation of initial symptoms, which are often misunderstood as insignificant health issues. For example, breathlessness can be due to stress or exertion, whereas blackouts can be confused with dehydration or fatigue. But such innocuous-sounding symptoms can be a harbinger of severe cardiovascular illness. Early detection and treatment are the key to preventing the advancement of heart disease and lowering the risk of sudden, disastrous cardiac events. Without timely and correct diagnosis, life-saving treatment windows are lost, and severe consequences become more likely. Raising awareness and enhancing diagnostic techniques are thus the critical first steps in confronting the global cardiovascular health emergency. © 2025 IEEE.

关键词： Cardiology

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Enhancing Urban Traffic Management Through Machine Learning Prediction Models for Sensor-Less Roads 8

Enhancing Urban Traffic Management Through Machine Learning ...

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8th International Conference on Intelligent Traffic and Transportation, ICITT 2024 - incorporating the 8th International Conference on Communication and Network Technology, ICCNT 2024

作者： de Souza Oliveira, Thamires Di Grande, Sarah Berlotti, Mariaelena Cavalieri, Salvatore Torrisi, Vincenza Calabrò, Giovanni Inturri, Giuseppe Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9781643685601

The process of urbanization on a global scale has generated a significant increase in metropolitan populations, which in turn brings with it a series of challenges for the management of transport infrastructure. In this context, the importance of accurate traffic flow prediction in urban traffic management systems stands out, with the city of Catania being used as the focus of this study. However, predicting traffic flow faces challenges, such as the high costs associated with acquiring extensive real traffic data, particularly when installing sensors on every road is not feasible. To address the issue of missing traffic flow data on roads without sensors, this study proposes an innovative approach based on machine learning, using the Long Short-Term Memory architecture and incorporating both sensor and floating car data. The results obtained are thoroughly analyzed and discussed, highlighting the ability of the LSTM model to provide accurate predictions of urban traffic, even with a limited amount of data available. These findings highlight the relevance of employing advanced machine learning techniques in the efficient management of urban mobility, aiming to improve the quality of life in cities and deal more effectively with the challenges of contemporary urban traffic. © 2024 The Authors.

关键词： Prediction models

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Optimized Mutual Authentication and AES Encryption Framework for Enhanced Security in Multi-Server Environments 2

Optimized Mutual Authentication and AES Encryption Framework...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kulkarni, Soham Sunil Kumar, Anant Gakhar, Suket Sharma, Vipin Kumar Arora, Sneha Department of Computer Science University of California Irvine Irvine United States Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Kurukshetra University Kurukshetra India Department of Electronics and Communication Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331530389

This paper introduces a secure authentication framework by applying Mutual Authentication Coupled with Optimised AES (Advanced Encryption Standard) Encryption to improve the security of the multi-server environment. By addressing inefficiencies in traditional mutual authentication protocols without encryption, this framework significantly improves both performance and resilience. Encryption time was reduced by 25% and authentication time by 16.67% while CPU (Central Processing Unit) usage was reduced by 23.08%, with a corresponding 21.43% improvement in memory efficiency. The proposed system demonstrates a 70% improvement in attack resistance. Dynamic revocation mechanisms mitigate unauthorized access and malicious activity in real time. The protocol is designed for resource-constrained environments to ensure secure, scalable, and efficient communication in multi-server architectures, further increasing system reliability and user trust in cloud-based services. © 2025 IEEE.

关键词： Authentication Protocol

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