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Coordinated Path Planning through Local Search and Simulated Annealing

ACM Journal of Experimental Algorithmics 2022年第6期27卷 1–14页

作者： Yang, Hyeyun Vigneron, Antoine Department of Computer Science and Engineering Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of

The third computational geometry challenge was on a coordinated path planning problem in which a collection of square robots need to move on the integer grid, from their given starting points to their target points, and without collision between robots, or between robots and a set of input obstacles. We designed and implemented three algorithms for this problem. First, we computed a feasible solution by placing middle-points outside of the minimum bounding box of the starting positions, the target positions and the obstacles, and moving each robot from its starting point to its target point through a middle-point. Second, we applied a simple local search approach where we repeatedly delete and insert again a random robot through an optimal path. It improves the quality of the solution, as the robots no longer need to go through the middle-points. Finally, we used simulated annealing to further improve this feasible solution. We used two different types of moves: We either tightened the whole trajectory of a robot, or we stretched it between two points by making the robot move through a third intermediate point generated at random. © 2022 Association for Computing Machinery.

关键词： Motion planning

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Recurrent Neural Network Analysis of Optometry Data

Recurrent Neural Network Analysis of Optometry Data

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Avinash Barua, Tarkeshwar Saxena, Rini Goswami, Manish Goyel, Parul Chandigarh Group of Colleges Chandigarh Engineering Department of Computer Science and Engineering 140307 India Roorkeee Institute of Technology Department of Computer Science and Engineering Roorkee India Chandigarh Pharmacy College Chandigarh Group of Colleges 140307 India M. M. Engineering College Maharishi Markandeshwar Mullana Computer Science & Engineering Haryana Ambala1307 India

ISBN: (纸本)9798331508432

This survey explores the potential of recurrent neural networks (RNNs) for analyse eye-trailling data, generally data obtained from Optometry systems. RNNs stand out at distinguishing patterns in consecutive data, making them appropriate for tasks like foretell future gaze, distinguishing areas of interest, and detecting anomalousness in gaze patterns. The abstract synopsis the process of probing Optometry data using RNNs. It highlights data pre-processing steps like cleaning, formatting, and normalization. The assortment of an LSTM network edifice and hyperparameter tuning for optimal execution are discussed. breeding and evaluation operation are explained[12], including using separate breeding, validation, and testing sets. Finally, the abstract mentions potential computer applications of the analysis, including human-computer interaction(HCI) (HCI) optimization and individual behavior understanding. © 2024 IEEE.

关键词： Recurrent neural networks

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Competent Diagnosis in Agriculture: Federated Learning CNNs for Avocado Leaf Diseases

Competent Diagnosis in Agriculture: Federated Learning CNNs ...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Kumar, Mukesh Thapliyal, Siddhant Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P. Greater Noida India

ISBN: (纸本)9798350361537

This research paper explores the novel application of federated learning with Convolutional Neural Networks (CNN) for avocado leaf disease classification across the four severity categories. Based on the details from four unique customers, the analysis shows how federated learning can capitalize in agricultural environments around local data variations and improve the global model's accuracy. Our approach is the only one to tackle the classification issues with four disease severity classes. However, the fusion of federated learning with CNNs secures data privacy by processing locality at each client and promotes model generalisability over the various datasets. In the results, for each client (kg-1 to kg-4), there are signs of a good level of precision, recall, F1-score support, and accuracy in the disease classification. In particular, the client kg-1 determined a macro average of 96.73%, a weighted average of 96.80%, and a micro average of 96.80% with an accuracy equal to. Client kg-3 was optimized with a macro-average efficiency score of 98.95%, a weighted average for performance scoring set to 0.7, and a micro-average. The peak performance was finally reached for client kg4 with a macro average of 99.15%, a weighted average of 98.73%, and a micro average age of ninety-six while maintaining accuracy. These results show the high performance of the federated learning CNN model in diagnosing avocado leaf diseases with great precision and present many opportunities to use this methodology for other agricultural applications. © 2024 IEEE.

关键词： Federated learning

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Blockchain-Driven Academic Learning Record Management in Higher Education: A Comprehensive Review of Methodologies, Applications, Benefits, and Challenges

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SN computer science 2025年第5期6卷 1-33页

作者： Chaudhari, Sheetal Shirole, Mahesh Computer Science and Engineering Department Sardar Patel Institute of Technology Andheri Maharashtra Mumbai 400058 India Computer Engineering and Information Technology Department Veermata Jijabai Technological Institute Matunga Maharashtra Mumbai 400031 India

Blockchain technology has the potential to revolutionize the education sector by improving the legitimacy and security of academic procedures. The academic learning record management records the learner’s academic qualifications and achievements. Keeping track of higher education academic learning records is essential to assess students’ credentials and advance their careers. The COVID-19 outbreak highlighted the need for flexible and collaborative learning systems. However, the processes of recording credit accumulation, verification, and transfer are complicated by the disparate credit schemes found in different universities. This creates the need for an academic credit management system. Secure learning record management is necessary because validating the professional and academic accomplishments listed on the resumé currently poses a challenge. Blockchain can remedy this scenario by guaranteeing data privacy, reducing the possibility of hacking, and offering transparent, traceable academic credentials as well as verification of other achievements. Despite these benefits, challenges are still associated with implementing blockchain in academic learning record management. These include handling multiple credentials with a unique identity, absence of smart contracts, on-chain and centralized storage, scalability problems, transaction costs, restricted control over student data, and inadequate student data recording standards. The main objective of this review is to examine recent studies on blockchain-driven academic learning record management by categorizing them into learning record management and academic credit management. This paper identifies research gaps and suggests further studies and practical applications to enhance transparency, efficiency, and trust in the education system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Academic bank of credits Academic credit management Blockchain Education Learning record management NEP 2020

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A Review on Rainfall Prediction Using Neural Networks 1

引用

4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Mandal, Sudipta Biswas, Saroj Kumar Purayastha, Biswajit Chakraborty, Manomita Kundu, Saranagata Computer Science and Engineering National Institute of Technology Assam Silchar India Computer Science and Engineering VIT-AP Campus Andhra Pradesh Amaravati India Computer Science and Engineering The University of Burdwan West Bengal Bardhaman India

ISBN: (数字)9789819900473

ISBN: (纸本)9789819900466

Rainfall prediction is a significant and demanding task in today's world. Due to the nonlinearity and complication, rainfall and climate require advanced computer modeling and simulation to predict accurately. This paper comes up with a comprehensive survey and a fair comparison of several types of neural network architectures for rainfall prediction and mentions the challenges of using traditional and advanced methods in doing this task. Finally, the paper discusses the utility of deep learning methods for rainfall forecasting. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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An Enhanced Deep Learning Model for Tomato Leaf Disease Prediction 8

An Enhanced Deep Learning Model for Tomato Leaf Disease Pred...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Dhivyaa, C.R. Nithya, K. Vignesh, T. Sudhakar, R. Kumar, K. Sathis Janani, T. Kongu Engineering College Computer Science and Engineering Erode India Kongu Engineering College Computer Technology-PG Erode India Nandha College of Technology Computer Science and Engineering Erode India Bannari Amman Institute of Technology Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Information Technology Sathyamangalam India

ISBN: (纸本)9798350396638

The rising need for tomato cultivation has raised concerns about detecting and diagnosing diseases affecting tomato plants. Tomato leaf disease prediction faces several challenges, including the variability of symptoms, limited availability of high-quality data, lack of standardization in disease identification and classification, and difficulty in accounting for environmental factors such as temperature and humidity. These challenges can make it challenging to develop accurate and reliable predictive models, particularly in developing countries. Detecting these diseases in the earlier stage is essential to prevent losses in yield and ensure food security. In current years, deep learning approaches have been increasingly utilized to tackle this issue. In this work, an improved deep learning model is introduced for predicting leaf diseases of tomato plants. The proposed model is based on a visual geometric group VGG-16 architecture, which has been proven effective in image classification tasks. The paper presents a Bayesian optimization method that involves fine-tuning a pre-trained VGG-16 model with a collection of images. To further increase the accuracy of the proposed work, data augmentation techniques are incorporated, such as random rotations, flips, and brightness adjustments, which increase the diversity of the training data. The diseases of tomato leaf prediction model are tested on both a publicly accessible dataset and custom dataset of tomato plant's leaf images. The experimental results demonstrate that the VGG-16 model with Bayesian optimization approach performs well than other methods. © 2023 IEEE.

关键词： Forecasting

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Optimizing Crop Recommendation System Using Threshold and Decision-Level Fusion

Optimizing Crop Recommendation System Using Threshold and De...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Umamaheswari, R. Kannan, E. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Nowadays crop recommendation systems face many obstacles in recommending suitable crops. A main challenge that has to be encounter is changes in environmental conditions and handling diverse dataset like soil and climate oriented dataset. Existing algorithm's like decision tree, random forest have limitations in adapting to local variability. To overcome this and to enhance the agricultural activities, this paper presents an innovative crop recommendation system based on threshold (CRST) values, through which crops are chosen by considering properties of soil, climatic parameters, and season factors such as Kharif, Rabi, and Zaid. Critical limit threshold values are set for most important features like fertility of soil, nutrition of the soil, pH, temperature, humidity, and rainfall to ensure optimal growth conditions. In order to recommend the suitable crop, a rule-based decision-fusion method is tied with confidence level analysis. To evaluate the effectiveness of the threshold method, ordinary least squares (OLS) regression is used. CRST model achieves an accuracy of 90% compared to existing algorithms. Integration of seasonal adaptation and threshold determination greatly enhances crop management decision-making and provides a strong answer to agricultural problems. © 2025 IEEE.

关键词： Temperature Rain Decision making Crops Humidity Soil Decision trees Recommender systems Random forests Faces

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Machine Learning based Human Stress Detection through Sleep Patterns 5

Machine Learning based Human Stress Detection through Sleep ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Santhosh Krishna, B.V. Mai, Kiran Aswini, S. Biju, J. Mohanraj, S. Kaliappan, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Bangalore Technological Institute Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technology Information Science and Engineering Erode Tamilnadu Sathyamangalam India KGiSL institute of Technology Artificial Intelligence and Data Science Tamilnadu Coimbatore India Lovely Professional University Division of Research and Development Punjab India

ISBN: (纸本)9798331504403

Stress is a critical factor in overall wellbeing, and understanding its patterns during sleep can provide valuable insights into improving sleep quality and managing stress-related health issues. Stress has become an increasingly prevalent issue in modern society, impacting both mental and physical well-being. The ability to accurately detect stress levels in individuals is crucial for timely intervention and management. Sleep patterns serve as a valuable indicator of stress, as disruptions in sleep quality and duration often correlate with heightened stress levels. Leveraging advancements in machine learning techniques, this study proposes a novel linaer regression based approach to detect human stress by analyzing sleep patterns extracted from wearable devices. The proposed system consists of several key components. Firstly, data collection involves the continuous monitoring of sleep parameters such as sleep stages, heart rate variability, and movement using wearable sensors. Next, feature extraction techniques are applied to transform raw sleep data into informative features that capture relevant aspects of sleep patterns indicative of stress. © 2024 IEEE.

关键词： Sleep research

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Autotrack: a framework for query-based vehicle tracking and retrieval from CCTV footages using machine learning at the edge

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International Journal of Information Technology (Singapore) 2023年第7期15卷 3827-3837页

作者： Sreedhar, Sreela Philip, Abin Oommen Sreeja, M.U. Department of Computer Science and Engineering Toc H Institute of Science and Technology Ernakulam India Department of Electronics Cochin University of Science and Technology Kochi India Department of Computer Science and Engineering-Cybersecurity Indian Institute of Information Technology Kottayam (IIIT Kottayam) Kottayam India

Automobile theft and hit and run incidents have become ever rampant than before urging the need for automated tracking systems. However, prompt detection of the vehicle involved in incident and recovery of the stolen vehicle is crucial as these are immediately forged to reduce the chances of getting caught. Hence, the proposed model puts forth a query-based framework to intelligently detect and track vehicles from CCTV surveillance footages using queries involving custom input features such as model, make, colour and license plate number. The model uses a combination of techniques for detecting the vehicle make and model, tracking vehicle, and for identifying the license plate number based on You Only Look Once (YOLO), Kalman filters, and Tesseract respectively. Further, the model performs vehicle detection and tracking from multiple surveillance videos which can ensure that the suspicious vehicle which is reported of either theft, license plate forgery or any unusual activity like hit and run cases is located immediately. The detection model is trained and deployed on edge device integrated to CCTVs and hence overcoming the drawbacks associated with a centralized system. The performance of the proposed vehicle detection model compared to baseline models on the basis of popular classification metrics precision, recall and f1-score proves the superiority of the proposed model compared to the existing ones. Additional experimental analysis of the entire framework further establishes the relevance of the combination of selected input features as well as emphasizes the significance of query-based models over its generic counterparts. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Edge computing Kalman filter Object detection Tracking Vehicle detection YOLO

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Diabetic Foot Ulcer Classification Using Deep Learning Approach 8th

Diabetic Foot Ulcer Classification Using Deep Learning Appro...

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

作者： Sivakamasundari, P. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

Podiatrist diagnosis and lesion localization are used as current testing approaches for Diabetic Foot ulcers (DFU). Current systems for automation concentrate on either categorization or division. One of the most common metabolic conditions, Diabetes (also called diabetes mellitus), is brought on by the pancreas incapacity to generate sufficient anti hyper-glycemic hormone to control glycemia levels. It may cause "Diabetic Peripheral Neuropathy," a collection of nerve diseases that can develop if the blood glucose level is left uncontrolled and unmanaged. Blood glucose levels may be efficiently maintained by recognising diabetes initially through constant surveillance and screenings. Using image processing methods such as data stretching with Deep Learning, image reduction and improvement, image division, and feature extraction, this paper offers a thorough approach for analysing thermal scans of the diabetic foot. By using these techniques, doctors may assess and track a patient's condition with fewer examinations. In this work, EfficientNetB3 architecture is compared with other Deep Learning models for accuracy in DFU identification. To create a powerful deep learning model, we gathered an extensive collection of 1,775 DFU photos. The basis of belief for this data gathering was created by two medical experts using annotator software to define the DFU region of interest. It took 48 ms to figure out a single image and 57.2 MB to validate the model. EfficientnetB3 got an average mean accuracy of 93%. This work demonstrates the effectiveness of Deep Learning for DFU localization in real-time, which could have improved with the use of a larger data set. © IFIP International Federation for Information Processing 2025.

关键词： Diagnosis

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