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12th International Advanced Computing Conference, IACC 2022

作者： Singh, Mahesh Kumar Singh, Akhilesh Kumar Singh, Pushpendra Kalpana Rishi, Om Prkash Department of Computer Science ABES Engineering College Ghaziabad201009 India Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Information Technology GL Bajaj Institute of Technology and Management Uttar Pradesh Gr Noida201306 India Department of Information Technology Raj Kumar Goel Institute of Technology Uttar Pradesh Ghaziabad201003 India Department of Computer Science Informatics University of Kota Rajasthan Kota324010 India

ISBN: (纸本)9783031356438

Due to growth of AI enabled IOT devices, their use in sports feature identification can be beneficial in terms of recognition quality. computer Vision is a powerful tool for moving object recognition and tracking. Football identification and recognition problem can be implemented using OpenCV libraries with the help of a programming language like Java, Python etc. OpenCV has special modules that contain specific algorithms and features for moving object recognition and tracking. Internet of things provides a way to connect hardware components through software implementation. In the proposed model the automatic camera module has been implemented through IoT with the help of a servo motor and raspberry pi. The main aim of this paper is to automate the work of a cameraman in a football match by automatically tracking and recognizing the football and broadcasting it with the help of an automatic camera. The initial loss was about 1749, and after 1600+ epochs iterations, the loss reduced to 0.09 in the final model. © 2023, Springer Nature Switzerland AG.

关键词： Cameras

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Annealing-Based Optimization for Selecting Training Space in Ensemble Learning 10

Annealing-Based Optimization for Selecting Training Space in...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Yulianti, Lenny Putri Trisetyarso, Agung Santoso, Judhi Surendro, Kridanto School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia School of Computer Science Bina Nusantara University Department of Mathematics Jakarta Indonesia

ISBN: (纸本)9798350329919

Ensemble learning integrates many learners to achieve better performance, however, it can increase the complexity dramatically. Redundant learners should be pruned to reduce ensemble complexity while allowing for increased accuracy. There are two common ways to achieve this, which include selecting the optimal training space and the optimal trained learners. This study contributes to reducing redundant learners by selecting the optimal training space using annealing-based optimization. The optimal training space was defined by 1) measuring the similarity between training spaces (clusters) and 2) searching for the best set of training spaces by minimizing the similarity between the selected training spaces. The proposed method was tested on 10 datasets from the UCI repository and compared to five state-of-The-Art random subspace methods. The results showed that the proposed method could reduce the size of the ensemble while significantly increasing its accuracy. © 2023 IEEE.

关键词： Annealing

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A secured GA-WPC framework for scheduling the independent tasks in cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 13003-13015页

作者： Dubey, Kalka Sharma, S.C. Kumar, Mohit Kumar, Prashant Nasr, Aida A. Cloud Computing and Wireless Sensor Lab Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais Uttar Pradesh Amethi229304 India Department of Information Technology Dr BR Ambedkar National Institute of Technology Punjab Jalandhar144011 India Information Technology Department Faculty of Computers and Informatics Tanta University Tanta Egypt

Cloud computing proved itself a powerful and popular platform for providing computing services. It offers on-demand services to users with highly efficient resources over the internet. With the exponential increase in the number of internet users and due to the high acceptance rate to use the cloud services in everything, the demand for cloud platforms has also increased significantly. This leads to heavy workloads on the servers, which is a difficult task to manage due to the heterogeneity and uncertainty of the resources. However, several traditional, heuristic, and evolutionary-based approaches have been proposed to solve the problem of scheduling. Existing state-of-art techniques and frameworks have solved the problem up to a limited level. Still, these solutions have not given any effective and concrete solution for the overall resource scheduling problem. Therefore, this paper tries to overcome the bottleneck of existing solutions by presenting a new framework named Genetic Algorithm Water Pressure Change (GAWPC) to solve task scheduling problems efficiently. The GAWPC framework exploited the feature of the Genetic Algorithm (GA) and Water Pressure Change optimization (WPCO) techniques to form a new hybrid mechanism. In the proposed framework, GA has been deployed to find the optimal solution for a particular population. WPCO has been used to optimize fitness function for a fast convergence rate. The computer-based simulation has demonstrated promising benefits like improved computation cost, make-span time, and load balancing when compared with the most recent existing baseline techniques using CloudSim. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning 1

Real Time Prediction Model for Air Pollution and Air Quality...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kumar, Rakesh Sharma, Bhisham Shekhar, Shashi Dhaou, Imed Ben Singhal, Saurabh GLA University Department of Computer Engineering & Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals' wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city. © 2023 IEEE.

关键词： Forecasting

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Ant Colony Optimization for Service Composition in Multi-cloud Environments 15

Ant Colony Optimization for Service Composition in Multi-clo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Maurya, Mahesh Vidhya, G. Akram, Faiz Saravanan, M. Venkata Ramana, K. Rajakumari, S. K.C. College of Engineering Department of Computer Engineering Mith Bunder road Thane East Maharashtra Kopri India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Jimma Institute of Technology Jimma University Faculty of Computing and Informatics Department of Computer Science Oromia Region Jimma Ethiopia Sri Eshwar College of Engineering Department of Ece Tamilnadu Coimbatore India Qis College of Engineering and Technology Department of Cse Andrapradesh Ongole India R.M.D. Engineering College Department of Mathematics India

ISBN: (纸本)9798350370249

the Ant Colony Optimization (ACO) algorithm is a famous metaheuristic technique that has been successfully applied in various optimization issues. It mimics the foraging conduct of ants to locate the shortest path among meals assets and their colony. Recently, ACO has been increasingly more applied for provider composition in multi-cloud environments. This involves the composition of offerings from different cloud carriers to satisfy the service necessities of a person. By way of using ACO, services can be correctly selected and composed from a couple of carriers, considering elements including cost, excellent, and availability. ACO has proven promising outcomes in enhancing the overall performance and reliability of carrier composition in multi-cloud environments, making it a precious technique in todays tremendously distributed computing landscape. © 2024 IEEE.

关键词： Distributed cloud

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Faba Bean Disease Detection Using Deep Learning Techniques

Faba Bean Disease Detection Using Deep Learning Techniques

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2023 International Conference on Cyber Management and engineering, CyMaEn 2023

作者： Salau, Ayodeji Olalekan Abeje, Bekalu Tadele Faisal, Abeer Naser Asfaw, Tesfa Tegegne Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Haramaya University College of Computing and Informatics Department of Information Technology Ethiopia Institute of Technology Faculty of Computing Bahir Dar University Department of Computer Science Ethiopia University of Sumer College of Computer Science and Information Technology Al-Rifai Iraq

ISBN: (纸本)9781665493291

Faba bean (Vicia faba L.) is an important cash crop for animal and human consumption in many countries, especially Ethiopia due to its high protein content and high rate of production. It also improves soil fertility with the help of bacteria, which forms nodules on its roots. Ethiopia is the second largest faba bean producer;however, according to research, the yield is generally low. Among the constraints contributing to low productivity of the crop, disease is the most important. Even though there are countermeasures such as the use of chemical sprays, which is used to reduce the damage or eliminate the disease, an effective disease identification technique is still required. Chocolate spot, faba bean gal, and rust are the most common diseases of faba beans. To effectively detect the disease, in this paper, we propose an end-to-end CNN model for training before and after the dataset is preprocessed. The CNN end-to-end model is trained with raw images and achieves 92.1% training accuracy. Also, the model is trained with preprocessed data and it achieves a training accuracy of 98.14% for the end-to-end CNN classifier. The results show that the proposed approach is suitable for faba bean disease detection and can be employed to detect diseases of other crops. © 2023 IEEE.

关键词： Convolutional neural networks

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IAQ Monitoring System Optimizing Data-Driven Sensor Placement

IAQ Monitoring System Optimizing Data-Driven Sensor Placemen...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Gabriel Filios Sotiris Nikoletseas Ioannis Stivaros Department of Computer Engineering and Informatics University of Patras Greece Computer Technology Institute and Press "Diophantus" Greece

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Indoor Air Quality (IAQ) significantly impacts people’s health and comfort in buildings. Although IAQ research spans two decades, a comprehensive assessment of factors affecting indoor air pollution remains elusive. Recent efforts focus on real-time monitoring using virtual sensing, a computational technique in engineering and data science. This paper presents a novel IAQ monitoring system emphasizing dynamic sensor placement for enhanced efficiency. The system employs random sensor positions and calculates measurement predictability, allowing identification and removal of less useful sensors, reducing data volume, and saving energy. Multiple reduction strategies are available, depending on the target number of edge devices or the desired maximum prediction error. Importantly, the system operates locally, without relying on internet connectivity. It consists of edge devices using air quality sensors, a gateway for data gathering and algorithm initiation, by training and evaluating multiple different machine learning techniques to determine point combination predictability. Deployed in two indoor settings, one with HVAC and the other naturally ventilated, the system’s effectiveness is assessed, shortcomings identified, and conclusions drawn for future work.

关键词： Training Sensor placement Machine learning algorithms Volume measurement Soft sensors Position measurement Prediction algorithms

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Realizing Predictive Maintenance in Production Machinery Through Low-Cost IIoT Framework and Anomaly Detection: A Case Study in a Real-World Manufacturing Environment

Realizing Predictive Maintenance in Production Machinery Thr...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Gabriel Filios Stylianos Karatzas Marios Krousarlis Sotiris Nikoletseas Stefanos H. Panagiotou Paul Spirakis Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece Civil Engineering Department University of Patras Greece Department of Computer Science University of Liverpool UK

Digital transformation and predictive maintenance are still some of the key challenges faced by the industrial sector as it moves towards Industry 4.0. However, the lack of resources, experienced personnel, and the absence of digital-savvy culture in manufacturing companies, often hinder the adoption of the required technologies. In this context, maintenance is a required process to ensure the smooth and continuous operation of the production, but to this day, engineers are still required to physically inspect equipment on site. By applying digital technologies, a predictive maintenance framework will enable the industry to maximize the life of the equipment and to act on time in order to prevent failures and thus reduce costs and optimize maintenance efficiency. In this effort, integrating such technologies and the required modern computing devices for monitoring legacy industrial equipment remains a challenge. Therefore, in this paper, we present a low-cost IIoT system for an AI-based predictive maintenance of machinery in a real-world industrial environment. In specific, a data collection system, consisting of a Raspberry Pi device and several sensors (namely, an accelerometer, temperature sensors and a microphone), was designed and developed. The collected data was then utilized to feed a deep-learning LSTM-autoencoder model for anomaly detection through a semi-supervised learning approach. Despite the small amount of data collected, the model is able to effectively detect anomalies. Finally, ways in which the existing system can be improved are proposed.

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Ultrasound Image Analysis in Breast Cancer: A Comparative Study of Decision Trees and Random Forests 16

Ultrasound Image Analysis in Breast Cancer: A Comparative St...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sivagami, S. Sathya, A. Afsin, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Bio Informatics Tamil Nadu Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for early detection and accurate diagnosis. Ultrasound imaging serves as a non-invasive diagnostic tool for identifying breast abnormalities, yet the complexity of these images presents significant interpretation challenges. This study explores the application of Machine Learning (ML) models, specifically Decision Tree (DT) and Random Forest (RF) classifiers, for the classification of breast cancer ultrasound images into benign, malignant, and normal categories. Data augmentation techniques are employed to address class imbalance and enhance model generalization using the Breast Cancer Ultrasonic Image dataset. Among preprocessing steps, pixel values were normalized to provide a consistent input for the models. RF model performed better than DT with an accuracy of 88.75% against an accuracy of 86.25% and those masks for ultrasound images contribute a lot to the overall classification accuracy. In summary, the results of our study can provide support for grand challenges in health and emphasize implications such as the potentiality of ML models to advance breast cancer diagnosis and it implies a necessity for an even further expansion that will allow complete patient outcomes optimization. © 2024 IEEE.

关键词： Radio frequency Accuracy Ultrasonic imaging Training data Predictive models Breast cancer Data models Decision trees Random forests Tumors

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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