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Tensor-Based Factorial Hidden Markov Model for Cyber-Physical-Social Services

IEEE Transactions on Services Computing 2025年第3期18卷 1825-1837页

作者： Lu, Zhixing Yang, Laurence T. Azman, Azreen Zhang, Shunli Zhou, Fang Hainan University School of Computer Science and Technology Hainan China Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang Malaysia Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China Huazhong University of Science and Technology School of Computer Science and Technology Huazhong China St. Francis Xavier University Department of Computer Science Canada

With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score. © 2008-2012 IEEE.

关键词： Viterbi algorithm

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Comprehensive Analysis on Anomaly Detection in Cybersecurity dataset using Deep Learning Algorithms

Comprehensive Analysis on Anomaly Detection in Cybersecurity...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Nidhishree, G. Vidyalakshmi, K. Vidyavardhaka College of Engineering Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning) Mysuru India Ssahe University Sri Siddhartha Institute of Technology Department of Computer Science and Engineering (Data Science) Tumakuru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cybersecurity has become very important due the rapid growth of digital technologies. It plays an important role in protecting computer systems and networks from potential attacks. Cybersecurity involves a set of technologies and algorithms that sometimes bring in threats to the security of computer networks, the end system, attacks on data, inappropriate access, changes and harm. Cyber-defense mechanisms involved at the host, application, network and data level require a constantly improved process to secure the system from any feasible attack, targeting data security and privacy. The network anomaly detection is challenging because of the complex network topologies and high dimensional data, leading to unbalanced performance, high false positive rates and difficulty of handling time-series data. Deep Learning (DL) methods appear as an essential element in network Intrusion Detection Systems (NIDS) to recognize malicious behaviors or anomalies. Anomaly detection and network security are extensively utilized for intrusion detection performed by utilizing Cyber security to reduce malicious behavior in the networks. DL based-based detection utilizes many algorithms, which are capable of capturing complex patterns, effective for high-dimensional data. In DL, the anomaly is solved by two ways: flow-based detection that analyzes the network flows and packet-level data to identify anomalies. © 2024 IEEE.

关键词： anomaly detection cyber security deep learning intrusion detection system privacy and security

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Toxic Comment Detection based on Improved Bi Directional Long Short Term Memory 1

Toxic Comment Detection based on Improved Bi Directional Lon...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kamma, Vidya Wbaid, Saef Sudhakar, K. Nisha, A. Saravanan, T. Neil Gogte Institute of Technology Department of Computer Science and Engineering Rangareddy Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, toxic comment detection evolved as important in detecting hatred and harmful content present in user-generated content. However, a few challenges are raised in detecting toxic comments like context sensitivity and missing toxic content. To overcome these issues, improved Bi-directional Long Short-Term Memory (Bi-LSTM) is proposed for toxic comment detection. Initially, data is preprocessed with lemmatization and stop words removal methods for converting the texts to its base form and eliminates the common words respectively. Then, the Word to Vector (Word2Vec) technique converts the words into a dense vector representation by capturing semantic relationships and contextual meanings. Finally, improved Bi-LSTM is employed for detecting the toxic comments by capturing both past as well as future context with its bidirectional architecture and focuses on important words within the context dynamically. From the results, proposed Improved Bi-LSTM model attained better outcomes in terms of accuracy (acc) of 94.32%, precision (pr) of 98.07%, and recall of 95.98% compared to existing Convolutional Neural Networks (CNN)-BiLSTMs. © 2025 IEEE.

关键词： Semantics

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Analysis of MFCC and Chromagram Features in Lightweight Bidirectional LSTM and Attention Models for Music Genre Classification 21

Analysis of MFCC and Chromagram Features in Lightweight Bidi...

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21st International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Huang, Renxiang Li, Xu Li, Jianping School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China Khoury College of Computer Science Northeastern university BostonMA02115 United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798331519254

Music genre classification is essential for organizing music libraries and enhancing recommendation systems. This paper evaluates four lightweight models combining Mel Frequency Cepstral Coefficients (MFCCs) and Chromagram features with Bidirectional LSTM and Multi-head Attention architectures. Using the GTZAN dataset, we found that models with Multi-head Attention, outperformed with MFCCs features, achieving a test accuracy of 85.6%. MFCCs excel in capturing timbral characteristics critical for genre classification, while Chromagram features proved less effective. Although overfitting occurred, future studies can focus on incorporating methods like data augmentation and regularization to improve performance. These lightweight models offer a balance between accuracy and computational efficiency, making them suitable for real-time applications. © 2024 IEEE.

关键词： Real time systems

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning 25

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Banimustafa, Ahmed Qattouz, Hazem Almatarneh, Sattam Mansour, Khalid Alazaidah, Raed Bulkrock, Olla Higher Colleges of Technology Computer Information Science Dept Abu Dhabi United Arab Emirates Princess Sumaya University for Technology Software Engineering Dept Amman Jordan Zarqa University Data Science & Artificial Intelligence Dept Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Arab-Open University Bahrain Faculty of Computer Studies Bahrain

ISBN: (纸本)9798331540012

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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RF-Based UAV Classification and Identification Using Machine Learning 10

RF-Based UAV Classification and Identification Using Machine...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Dubey, Nidhish Sai Nithin, Nanduri Mahathi Tripathi, Shrivishal Dr. Spm Iiit Student of Data Science and Artificial Intelligence Naya Raipur India Dr. Spm Iiit Faculty of Electronics and Communications Engineering Naya Raipur India

ISBN: (纸本)9798350382471

In today's market, the position of Unmanned Aerial Vehicles (UAVs) are considered ubiquitous. They are used for hobby recreation and are also customized to meet clients' unique industrial needs, including in fields like the construction and agriculture sectors. Nonetheless, UAVs' involvement in criminal and illegal activities is also increasing. Hence, effective detection and identification of UAVs have become an essential field in research. In order to effectively recognize and identify UAVs, a new machine learning-based technique is proposed in this study. To be exact, radio frequency (RF) data pretreatment and feature extraction phases are described together with an enhanced approach for UAV identification and detection utilizing ensemble learning that utilizes hierarchical notions. The RF signals are filtered to increase the efficiency of the output. Raw signals from numerous UAVs in various operating states, spanning the modes of on, off, video taking, flying, connected, and hovering, were comprehensively researched using publicly accessible datasets. Additionally, we developed an algorithmic solution to find and identify intrusive drones using RF datasets. The algorithm has been trained to detect if a UAV is in range, immediately identify the UAV type, and accurately determine the mode of flight of said discovered UAV. © 2023 IEEE.

关键词： detection identification machine learning radio frequency unmanned aerial vehicles

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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A Deep Learning Framework for Prediction of Heart Diseases from Clinical Observations 2

A Deep Learning Framework for Prediction of Heart Diseases f...

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2nd International Conference on Augmented intelligence and Sustainable Systems, ICAISS 2023

作者： Thomas, Daze Mohan, M. Narasimharaj, V. Yamuna, S. Sns College of Engineering Artificial Intelligence & Data Science Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India

ISBN: (纸本)9798350325799

Among today's youth, heart disease has emerged as a major health concern. Many forms of cardiac disease can be traced back to bad habits and clinical characteristics. Many other models have been proposed by researchers to aid in the diagnosis of various conditions, however the vast majority either has poor accuracy performance or are overly complex to implement in real-time clinical settings. In addition, these models tend to function correctly for smaller class sets, but their performance degrades with an increase in class count. To facilitate answer these issues, this study suggests creating a novel, effective, and extensible strategy for disease prediction. This employs a deep learning technique, which boosts multiple sets of clinical data using a combination of StackingCV classifier-bagging and feature-boosting. Feature variance is increased by augmenting these vectors with a boosting of classifiers at the outset. AdaBoost strategy (ABA), Support-Vecto-Classifier (SVC), and Extreme-Gradient-Boosting (XGBoost) are then applied to these characteristics to help improve accuracy performance at the cost of minuscule increases in computational delays. Single illness classifier accuracy was 93.44% and multiple disease classifier accuracy was 60.5% when the model was tested using datasets from the University of California, Cleveland. When contrasted to various state-of-the-art methods and classifiers, such as a Deep-Neural-Network (DNN), Random-Forest (RF), XGBoost, &SVC, the suggested model was shown to be at least 3.9% more accurate. Thus, it is appropriate for use in live medical settings. © 2023 IEEE.

关键词： Adaptive boosting

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Optimizing Clustering Approaches in Cloud Environments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 70-94页

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kreishan, Yazeed Alsarhan, Ayoub Idhaim, Hasan Qahmash, Ayman Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Computer Science College King Khalid University Abha Saudi Arabia

This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fault tolerance

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