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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Shambhab Chaki Pratyusha Rakshit Archana Chowdhury Department of Electronics and Telecommunication Engineering Jadavpur University India Department of Computer Science and Engineering Christian College of Engineering and Technology Bhilai India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The paper proposes a novel machine learning approach for early prediction of risk of a patient suffering from severe kidney-related diseases (KD). The training phase consists of two steps. First, the records of the already existing patients with KD are divided into groups based on the similarity of the sequences of their clinical activities, including diagnoses, lab tests and medications. Next, a Markov chain model is employed to mathematically represent the clinical activity-sequences of patients in each group. The prediction phase also consists of two steps. First, the support of each group to the clinical-activities of the unknown patient P is evaluated. P is then assigned with the group with the highest support. Next, the Markov chain model of the winning group is used to predict the possible finite sequence of the clinical activities of P in future. Comparative study shows better performance of the proposed algorithm over other state-of-the-art algorithms.

关键词： Training Technological innovation Machine learning algorithms Shape Machine learning Predictive models Prediction algorithms

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Satellite Image–Based Ecosystem Monitoring with Sustainable Agriculture Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 764-773页

作者： Mulakaledu, Ajjanna Swathi, Baswaraju Jadhav, Makarand Mohan Shukri, Shakeerah Mohd Bakka, Vinod Jangir, Pradeep Department of English Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Bengaluru India Department of Electronics and Telecommunication NBN Sinhgad Technical Institutes Campus Pune 411041 India Management and Science University Selangor Shah Alam Malaysia Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Applied Science Research Center Applied Science Private University Amman Jordan

Understanding the variations in soil fertility and crop growth across time and geography is crucial for understanding the agricultural environment. Satellite and unmanned aerial remote sensing are the two main types of remote sensing methods used in agroecosystem monitoring. Through the collection of remote sensing photos, it is able to monitor as well as control agro-ecosystem environment in real-time. Spatial choice constraints are lessened by high-decision satellites, and satellite imagery processing is improved by use of artificial intelligence (AI) and machine learning methods, which enables computerised crop identification, disease diagnosis, and yield calculation. This research proposes novel technique in satellite image–based sustainable agriculture analysis with ecosystem monitoring using machine learning techniques. Here, the input is collected as satellite image and processed for noise removal and normalisation. Then, this image feature has been selected for analysis of crop growth using Gaussian belief kernel component analysis and classified using recursive Bayes probabilistic vector neural networks. Experimental analysis has been carried out in terms of training accuracy, precision, sensitivity, F-1 score, and AUC for various satellite image dataset. Proposed technique attained precision of 94%, sensitivity of 96%, AUC of 93%, training accuracy of 97%, and F1-score of 95%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Crop Growth Analysis Ecosystem Monitoring Machine Learning Techniques Satellite Image Sustainable Agriculture Analysis

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Machine Learning Approach to IoT- Based Water Quality Monitoring 5

Machine Learning Approach to IoT- Based Water Quality Monito...

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5th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Lin, Min-Tien Hwang, Sz-Chwun John Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Program in Engineering Science Hsinchu City Taiwan Chung Hua University Department of Landscape Architecture Hsinchu Taiwan

ISBN: (纸本)9798350320978

Water resources are inevitable for human survival but untreated wastewater harms the environment. Thus, ongoing monitoring of water quality is necessary to identify pollution sources and prevent further damage. For such monitoring, an IoT water quality monitoring system was developed using Arduino technology to collect and transmit data to MQTT Brokers and store it in a database. The data is presented on a monitoring webpage. Three machine learning methods (Random Forest, ANN, and LightGBM) were used for backend analysis and prediction. LightGBM was found to have the highest prediction accuracy for NH3, pH, ORP, and temperature. The research contributes to reducing the need for frequent and costly data collection by using an IoT system for real-time monitoring and employing machine learning predictions to compensate for missing data. This approach provides a more efficient and effective method for analyzing and predicting water quality. © 2023 IEEE.

关键词： Internet of things

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Telematics Work Field Review Text Classification Using the Naïve Bayes Method

Telematics Work Field Review Text Classification Using the N...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Maesya, Aries Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Indonesia

ISBN: (纸本)9781665468763

Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%. © 2022 IEEE.

关键词： Telematics

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A Machine Learning Technique for Detection of Diabetes Mellitus 5

A Machine Learning Technique for Detection of Diabetes Melli...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Awoniran, O.M. Oyelami, M.O. Ikono, R.N. Famutimi, R.F. Famutimi, T.I. Bowen University Computer Science Program Iwo Nigeria Obafemi Awolowo University Department of Computer Science and Engineering Ile-Ife Nigeria

ISBN: (纸本)9781509064229

The need for early detection of diabetes mellitus has led to the development of various intelligent systems using machine learning and artificial intelligence for the recognition of the presence of the disease. However, most of the techniques have yielded a comparatively lower accuracy. This research applied data science techniques to a dataset of diabetes mellitus to improve the accuracy of the prediction of the disease. This was achieved by pre-processing the data with dummy categories and applying principal components analysis for reduced dimensionality. Support vector machine, random forest classifier, and deep neural networks were then used to train the system. Support vector machine, random forest classifier, and deep neural networks yielded accuracies of 0.76, 0.77, and 0.89 respectively. Correspondingly, deep neural networks yielded the highest accuracy. The study concluded that better pre-processing will improve the accuracy of machine learning algorithms in the prediction of diabetes mellitus. © 2022 IEEE.

关键词： Deep neural networks

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids 48

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Davis, Katherine Serpedin, Erchin Texas A and M University Department of Electrical and Computer Engineering College StationTX United States Texas A and M University at Qatar Electrical and Computer Engineering Program Doha Qatar Tennessee Technological University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9781728163277

False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-Task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5-100% and 99-100%, respectively, presenting improvements of 5-30% compared to benchmarks. © 2023 IEEE.

关键词： Smart power grids

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Applying ant colony optimization algorithm to solve flying probe inspection problem

Applying ant colony optimization algorithm to solve flying p...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Chih-Yung Chang, Yu-Ming National Pingtung University of Science and Technology Program in Artificial Intelligence and Mechatronics Pingtung Taiwan Shu-Te University Department of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

This paper applies ant colony optimization (ACO) algorithm for the dual-pin flying probe circuit board inspection optimal path searching problem. First, the proposed approach creates a representation for circuit inspection path, which groups and encodes the measurement endpoints into a sequence. Moreover, the ant colony algorithm with the sequence can be used to solve the complex circuit optimal problem. The experimental results show that the best solutions of ACO are performed better than other algorithms. © 2023 IEEE.

关键词： Inspection

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Recursive Additive Attention Based Convolutional Neural Network Model for Plant Disease Detection 21

Recursive Additive Attention Based Convolutional Neural Netw...

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21st IEEE India Council International Conference, INDICON 2024

作者： Divakar, Subham Priyadarshini, Rojalina C.V. Raman Global University Department of Electronics and Telecommunication Bhubaneswar India C.V. Raman Global University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350391282

In recent years, the need for accurate and efficient image classification has become increasingly important across various domains, particularly in agriculture where early and precise identification of plant diseases can significantly impact crop yield and food security. Traditional Convolutional Neural Networks (CNNs) have proven effective in feature extraction but often struggle with capturing complex contextual relationships within images. To address this limitation, we present a novel approach that integrates a recursive additive attention mechanism within a CNN-based encoder-only architecture. Our approach leverages the power of attention mechanisms to focus on relevant features while recursively and additively refining this attention over multiple iterations, leading to more nuanced and contextaware feature extraction. This innovative combination enhances the model's ability to differentiate between subtle variations in plant disease symptoms, resulting in superior classification peformance. Evaluated on a comprehensive plant disease dataset, our model achieves an overall accuracy of 98.61 percent, a Cohen's Kappa Score of 0.9812, and high precision, recall, and F1 scores across all classes. Additionally, the model exhibits a low log loss of 0.0523, underscoring its robust probabilistic predictions. These results demonstrate the effectiveness of our approach in improving classification accuracy and reliability, offering significant advancements over traditional methods. This work not only contributes valuable insights to the field of image classification but also showcases the potential of advanced attention mechanisms in enhancing the performance of practical applications. © 2024 IEEE.

关键词： Deep Learning Disease Prediction Plant Disease Detection Recursive Additive Attention

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Chest Radiographs Based Pneumothorax Detection Using Federated Learning

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computer Systems science & engineering 2023年第11期47卷 1775-1791页

作者： Ahmad Almadhor Arfat Ahmad Khan Chitapong Wechtaisong Iqra Yousaf Natalia Kryvinska Usman Tariq Haithem Ben Chikha Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Prince Sattam bin Abdulaziz University AlKharjSaudi Arabia

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest *** most persistent disease,as well as one that necessitates particular patient care and the privacy of their health *** radiologists find it challenging to diagnose pneumothorax due to the variations in *** learning-based techniques are commonly employed to solve image categorization and segmentation ***,it is challenging to employ it in the medical field due to privacy issues and a lack of *** address this issue,a federated learning framework based on an Xception neural network model is proposed in this *** pneumothorax medical image dataset is obtained from the Kaggle *** preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s ***-max normalization technique is used to normalize the data,and the features are extracted from chest Xray *** dataset converts into two windows to make two clients for local model *** neural network model is trained on the dataset individually and aggregates model updates from two clients on the server *** decrease the over-fitting effect,every client analyses the results three *** 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%*** experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

关键词： Privacy preserving pneumothorax disease federated learning chest x-ray images healthcare machine learning deep learning

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