检索结果-内蒙古大学图书馆

A new hybrid optimization algorithm for maximizing area coverage in wireless sensor networks

Australian Journal of Electrical and Electronics Engineering 2024年第4期21卷 449-464页

作者： Swati, S. Mallapur, Sujata V. Department of Artificial Intelligence & Machine Learning Sharnbasva University Kalaburagi India

The rapid advancements in technology influence develop our lives day-by-day. However, the area coverage issue exhibited by sensor nodes in WSN still seems a complicated issue even if the area coverage enhancement models are developed. This research work is developed on the basis of implementing an efficient area coverage maximising approach with an advanced optimisation model, Coati Modernized Beluga Whale Optimisation (CMBWO). The whole process of the area coverage maximising approach revolves in two important steps: (i) Sensor node coverage model and (ii) Area coverage optimisation. In the first step, a 2D WSN monitoring area set-up is considered and in which target monitoring points are assumed to solve the coverage issue. Most importantly, the distance between the sensor node and the target monitoring point is calculated using weighted Manhattan distance evaluation instead of conventional Euclidean distance evaluation. This improvement is introduced to calculate the precise position of the target monitoring point from the position of the sensor node. The next, area coverage optimisation step is handled by the CMBWO algorithm for maximising area coverage in WSN. This hybridisation is possible by modernising BWO via COA. This efficient hybridisation led to maximising the area coverage of sensor nodes in WSN. ©, Engineers Australia.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Integrated Q-learning with Firefly Algorithm for Transportation Problems

EAI Endorsed Transactions on Energy Web

引用

EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Pratiba, K.R. Ridhanya, S. Ridhisha, J. Hemashree, P. Department of Artificial Intelligence and Machine Learning Coimbatore Institute of Technology TamilNadu Coimbatore India

The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes. Copyright © 2024 K. R. Pratiba et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhancing coffee bean classification: a comparative analysis of pre-trained deep learning models

引用

Neural Computing and Applications 2024年第16期36卷 9023-9052页

作者： Hassan, Esraa Department of Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

Coffee bean production can encounter challenges due to fluctuations in global coffee prices, impacting the economic stability of some countries that heavily depend on coffee production. The primary objective is to evaluate how effectively various pre-trained models can predict coffee types using advanced deep learning techniques. The selection of an optimal pre-trained model is crucial, given the growing popularity of specialty coffee and the necessity for precise classification. We conducted a comprehensive comparison of several pre-trained models, including AlexNet, LeNet, HRNet, Google Net, Mobile V2 Net, ResNet (50), VGG, Efficient, Darknet, and DenseNet, utilizing a coffee-type dataset. By leveraging transfer learning and fine-tuning, we assess the generalization capabilities of the models for the coffee classification task. Our findings emphasize the substantial impact of the pre-trained model choice on the model's performance, with certain models demonstrating higher accuracy and faster convergence than conventional alternatives. This study offers a thorough evaluation of pre-trained architectural models regarding their effectiveness in coffee classification. Through the evaluation of result metrics, including sensitivity (1.0000), specificity (0.9917), precision (0.9924), negative predictive value (1.0000), accuracy (1.0000), and F1 score (0.9962), our analysis provides nuanced insights into the intricate landscape of pre-trained models. © The Author(s) 2024.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Image Recognition for Wildlife Conservation

Image Recognition for Wildlife Conservation

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Sridharan, S. Yuvan Shankar, S. Sriram, M. Yashvanth, K.P. Department of Artificial Intelligence and Machine Learning Coimbatore India Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

This study adopts an empirical approach to evaluate the efficacy of image processing methods in conservation efforts for animals. The initial phase involves the collection of data from various sources within the natural ecosystem, including images captured through camera traps, drones, and other monitoring devices. This comprehensive and diverse dataset is then processed using advanced Image Processing Tools. A key strength of this methodology lies in its use of machine learning algorithms, particularly YOLO v8 (You Only Look Once) and Support Vector machines (SVMs). These techniques are suitable for wildlife conservation because of their capacity to analyze complex visual data and detect patterns indicative of species presence, behavior, and habitat conditions. The paper outlines the application of YOLO v8 and SVMs to the collected image data, enabling accurate identification, real-time detection, and classification of wildlife species. This capability is essential for monitoring populations, tracking movements, and identifying potential threats, thus facilitating timely conservation actions. Furthermore, the research extends its evaluation by comparing the results of the machine learning approach with traditional ecological survey methods, offering a comprehensive assessment of the effectiveness of image processing in wildlife conservation. © 2025 IEEE.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

Background initialization in video data using singular value decomposition and robust principal component analysis

引用

International Journal of Computers and Applications 2023年第9期45卷 600-609页

作者： Gowda, Vishruth B. Gopalakrishna, M.T. Megha, J. Mohankumar, Shilpa Department of Information Science and Engineering SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India

Background initialization is used in video processing applications to extract a scene without the foreground scene. In recent times, the issue of background initialization has gained researchers’ attention in different fields such as video surveillance, video segmentation, computational photography, and so on. The initialization of the background is affected due to different complex dissimilarities such as shadow, intermittent movement, illumination, camera jitter, and clutter. To overcome the aforementioned issues, this paper proposes a decomposition using the combination of the Singular Value Decomposition (SVD) and Robust Principal Component Analysis (RPCA) for Singular Spectrum Analysis (SSA) to perform an effective background initialization. The incorporation of RPCA in SVD is used to overcome the issues related to non-Gaussian noise and it also uses an effective structural knowledge of the video input i.e. sparse and low rank matrix which improves the Peak-Signal-to-Noise-Ratio (PSNR) of the background image. The SBI dataset was used to analyze the performances of SSA-SVDRPCA. The SSA-SVDRPCA is analyzed using MultiScale Structural Similarity Index (MSSSIM), Average gray-level error (AGE), Percentage of clustered error pixels (pCEPS), Percentage of error pixels (pEPs), and PSNR. The existing approaches such as Background Initialization Singular Spectrum Analysis (BISSA) and Quaternion-based Dynamic Mode Decomposition (Q-DMD) are used to compare with the SSA-SVDRPCA method. The PSNR of the SSA-SVDRPCA for Board class is 30.39 dB which is higher than the BISSA and Q-DMD. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Quantales: Bridging Realms with Quantum Generative Adversarial Networks and Transformers in Educational Content Creation 3

Quantales: Bridging Realms with Quantum Generative Adversari...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Meeradevi Sriraksha, P.J. Vishal, Rahul K. Kulkarni, Pooja S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331515911

Traditional teaching methods in Indian schools often lack engaging elements, hindering students' full comprehension of concepts and fostering a tendency to memorize rather than understand. Existing solutions fail to provide effective visualization-based interactive learning. To address these issues, we propose an interactive application-based solution utilizing generative AI image generation models that can be used by students and teachers. Our application employs interactive story generation, allowing students to prompt creative texts and gain a deeper understanding of concepts. Trained on the school curriculum, this tool facilitates self-study, empowering students to understand complex topics independently. The application extends to classroom use, enabling teachers to incorporate interactive and application-based learning alongside traditional methods. Leveraging Quantum Generative Adversarial Networks (QGANs) and transformer-based models, the system analyses user input, extracts key elements, and creates dynamic story graphs for engaging narrative experiences. The integration of quantum computing enhances the application's capabilities, providing a novel approach to education through dynamic *** conclusion, the proposed solution seeks to bridge the gap in traditional teaching methods by introducing an innovative and interactive approach to education using Quantum Generative Adversarial Networks. This holistic solution addresses the limitations of current educational practices, offering a transformative learning experience for students and teachers alike. © 2025 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Revelatory Insights into Parkinson’s: Hand Gestures Deciphering with Mobilenet SSD 8th

Revelatory Insights into Parkinson’s: Hand Gestures Deciphe...

引用

8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Balaji, V.S. Sangeetha, K. Anand, V. Aatrey Velayutharaj, J. Abishek Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031736162

Hand gestures have evolved into a natural and intuitive means of engaging with technology. Parkinson’s disease is a neurological ailment that worsens with time. Sophisticated deep learning-based system for the precise identification of Parkinson’s Disorder is developed through the interpretation and analysing of hand gestures exhibited by affected individuals. Relevant features are extracted from the images, employing computer vision techniques, enabling the analysis of hand movements. A deep learning model is trained to recognize distinctive motor symptoms associated with the disorder. Object detection algorithms are employed to pinpoint specific deviations in finger and hand positions, thereby quantifying tremors, bradykinesia, and other relevant motor symptoms. The approach lies in the utilisation of a dataset comprising 40 images for each Parkinson’s hand gesture, enabling robust model training. The Mobilenet SSD algorithm for object detection to find the specific deviations in finger and hand positions. The model exhibits a high level of accuracy in distinguishing between individuals with the possibility of Parkinson’s disorder and those without, based on the subtle motor symptoms exhibited in their hand gestures. © IFIP International Federation for Information Processing 2025.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Precision Exercise Monitoring Through Advanced Body Language Detection Using Computer Vision 8th

Precision Exercise Monitoring Through Advanced Body Language...

引用

8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Balaji, V.S. Sangeetha, K. Ganapathy, P. S. Anirudh Banu, M. Shafiya Kumar, S. Dinesh Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031736162

The paper aims to assist individuals in performing physical exercises with precision and safety. This model delivers real-time feedback on an individual’s workout style by utilising powerful computer vision and machine learning technology. It uses deep learning models for position estimation to recognise and track essential body features while exercising. It classifies workouts and their phases, such as "down" and "up," based on a continual monitoring of the user’s body position. Advanced computer vision algorithms are used to analyse video frames from the camera feed in order to extract critical data about the user’s form. It can recognise and classify certain workouts such as the deadlift, squat, and push-up by continually monitoring the user’s body position. The system provides useful information such as the current stage of the exercise, the number of repetitions, and the chance of correct performance. The model can recognize and detect the exercises performed by the person and it also iterates the number of times the exercises are done and it also detects the position of the body of the person. Healthcare professionals who want to promote safe and successful exercise regimens. © IFIP International Federation for Information Processing 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Mental Health Assessment using Emotion Detection for Real-Time Analysis

AI-Powered Mental Health Assessment using Emotion Detection ...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vijay, K. Raghakeerthana, R. Milinda, Renata Rachael Thusheel, S. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331512965

This paper is an AI-driven mental health assessment tailored to each patient's distinct emotional needs, addressing the global mental health crisis. Using advanced machine learning and natural language processing, it aims to deliver personalized guidance for psychological and physical wellness activities, along with tools for tracking individual progress. The papers employ exploratory case studies and knowledge discovery techniques to evaluate the effectiveness of these interventions. Data mining methods reveal hidden patterns within mental health datasets, with the potential to reshape therapeutic paradigms. Expected outcomes include enhanced accessibility to mental health resources, early intervention, personalized progress monitoring, improved quality of life, and reduced stigma. By integrating sophisticated data processing, machine learning, and empathetic user interaction, it operates by redefining mental health support. The system's architecture is designed to promote both emotional and cognitive well-being, fostering positive change in mental health care delivery. © 2025 IEEE.

关键词： Computer Vision conversational bot Convolution neural network Emotion detection machine learning natural language processing Real-Time Analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Operational Efficiency and Security in Multi-Sectioned Businesses with Edge AI through Real-Time Analytics

Enhancing Operational Efficiency and Security in Multi-Secti...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Vijay, K. Jeyanth, V. Kamalesh, S. Dheva Prasath, D. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

The goal of this project is to disrupt the way businesses monitor their operations by integrating IoT and machine learning to gain a deeper understanding of customer behavior and improve operational efficiency. Traditional surveillance systems create lots of data to store and analyze, but they do not provide actionable intelligence. CCTV cameras and edge devices strategically placed throughout a facility capture real-time footage, identify visitor movement patterns, and represent critical heat zones on a heat map. This data-driven process enhances resource allocation, enhances customer experience, and enables proactive improvements in operational workflow. Edge devices enable real-time anomaly detection, such as theft or staff sitting, without compromising privacy, as the device retains all information and performs all processing locally. Our next-gen solution provides businesses with an all-inclusive monitoring system that solves today's most pressing security and operational issues, ultimately delivering better business results. © 2024 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：