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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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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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A study on the role of deep learning in the detection and diagnosis of epileptic seizures 4th

A study on the role of deep learning in the detection and di...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Chauhan, Riya Kaur, Sukhpreet Saini, Dapinty Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

In today’s world, epileptic seizures which is a chronic neurological disorder causes rapid disruptions to a person’s lifestyle. It is very common and the number of cases that involve patients that remain uncurable even after medication or surgery exceed than 30%. Predicting a seizure before it happens, however, can aid in preventing it. Screening techniques have been put forth to identify epileptic seizures utilizing magnetic resonance imaging (MRI) and electroencephalography (EEG) modalities. Deep learning (DL) is one of the many subfields that make up artificial intelligence. An EEG is one of the primary methods used to diagnose epilepsy. Typically, a human specialist uses the multi-channel electroencephalogram to identify distinct patterns that indicate the presence of epileptic activity. Many attempts are made to automate this activity using both conventional and Deep Learning (DL) techniques because it is challenging and time-consuming. © 2025 the Author(s).

关键词： Electroencephalography

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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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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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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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Deep Belief Network Optimization Using PSOGAA Algorithm for Efficient Crop Recommendation 5th

Deep Belief Network Optimization Using PSOGAA Algorithm for ...

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5th International Conference on Cognitive Computing and Information Processing, CCIP 2023

作者： Madhuri, J. Indiramma, M. Nagarathna, N. Department of Computer Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9783031607240

The agricultural sector plays a crucial role in the development of an emerging economy, providing employment to a significant portion of the population. Selecting the most suitable crops based on specific weather and soil conditions is vital to maximize yield and ensure profitability for farmers, contributing to global food security. In this context, this paper introduces a crop recommendation model utilizing Deep Belief Networks (DBN), fine-tuned by a hybrid optimization approach combining Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Adam optimization. To enhance the efficiency of DBN-based crop recommendation model, this paper proposes the PSOGAA algorithm, a novel hybrid optimization algorithm designed to optimize the DBN’s network structure. Subsequently, genetic operators featuring self-adjusting crossover and mutation probabilities are applied to further refine the PSO and explore a global optimization solution. Ultimately, this global optimization solution is employed to construct the network structure of the crop recommendation model. Experimental results demonstrate the superior performance of the proposed algorithm compared to other DBN optimization techniques, showcasing an average classification accuracy improvement of at least 2.3%. This emphasizes the effectiveness of the PSOGAA algorithm as an efficient DBN optimization technique. Focusing on four major crops in India—rice, sugarcane, maize, and finger millet—the crop recommendation model’s outcomes are compared with those of the DBN model optimized using alternative algorithms. The experimental findings highlight that the proposed hybrid optimization algorithm surpasses other optimizers, exhibiting enhanced accuracy in crop recommendations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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Detection of Cyber Attack in IoT Based Model Using ANN Model with Genetic Algorithm 5

Detection of Cyber Attack in IoT Based Model Using ANN Model...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Srivastava, Animesh Sharma, Hemendra Shanker Rawat, Rishikesh Garg, Navin Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Coer University Department of Computer Science & Application Roorkee India Bansal College of Engineering Bhopal India Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350383522

Thanks to the IoT, our everyday lives are now more accessible, connected, and convenient than ever before. Because it facilitates the effortless transfer of massive amounts of data between interconnected devices, it leaves the network vulnerable to various forms of intrusion and attack. As the number of interconnected devices grows, the need for reliable attack detection systems to forestall malicious actions becomes paramount. This paper introduces a strategy for detecting attacks on the Internet of Things (IoT) that integrates Genetic Algorithms (GA) and Artificial Neural Networks (ANN) models. This paper introduces a model for an artificial neural network that uses genetic algorithms to detect attacks on the Internet of Things. While the GA adjusts the hyperparameters of the ANN model, the ANN model is responsible for determining whether network traffic is malicious or not. To evaluate the proposed system, an Internet of Things network traffic dataset was utilized. As demonstrated by the outcomes, the system has a commendable rate of detection. The system can spot DoS attacks. The results show that our method is great at finding attacks on the Internet of Things. we achieved a 99.3 percent accuracy rate with our suggested ANN with GA. © 2024 IEEE.

关键词： artificial neural networks attack detection deep learning Internet of Things

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Simplified Model Predictive Current Control of a Mono-Inverter Dual Parallel Permanent Magnet Synchronous Motors

Simplified Model Predictive Current Control of a Mono-Invert...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lee, Hyung-Woo Lee, Kyo-Beum Ajou University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350376067

This paper proposes the simplified model predictive current control (MPCC) of a mono-inverter dual parallel (MIDP) permanent magnet synchronous motors (PMSMs). In the proposed MPCC method, one of the two parallel PMSMs is selected as the master PMSM to be object of control. The output torque of the slave PMSM is controlled with the master PMSM by utilizing the current relationship between the two motors. The application of the MPCC is operated through the d-axis current of the master PMSM. The candidate voltage vectors are generated within a segmented voltage vector plane for the reduction of computational burden and improvement of torque quality. To reduce the torque ripple, the voltage vectors applied to the inverter are conclusively determined using discrete space vector modulation (DSVM). The validity of the proposed method is verified through simulation results. © 2024 IEEE.

关键词： Dual PMSM drive Model predictive current control Mono-inverter Surface-mounted permanent magnet synchronous motor

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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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