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IAENG International Journal of computer Science 2021年第2期48卷 1-13页

作者： Sun, Wei-Zhong Guo, Fu-Jun Wang, Jie-Sheng Chen, Lin Wei, Dong Du, Xin-Feng A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China An associate professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

A colony is a group of microorganisms produced by the growth and reproduction of a single microorganism species. A method of colony image feature extraction, essential dimension estimation and dimension reduction based on digital image processing technology was proposed. Firstly, based on the HSI image color moments (first, second and third order moments), nine color characteristics of the colony images were extracted. Based on gray-level co-occurrence matrix (GLCM), twenty texture features of colony images were obtained. Then, three essential dimension estimation methods, namely, the correlation dimension estimator, the maximum likelihood estimator and the packing numbers estimator, are used to estimate the dimension of the inner low-dimensional structure of the high-dimensional data of the colony images. The results of different dimension estimators were combined with six different dimension reduction techniques (PCA, LDA, MDS, ISOMAP, SNE and NCA) respectively to form 18 different data dimension reduction methods. Based on the distance criterion function, an optimal dimension reduction method for the high-dimensional feature data of the colony images corresponding to the maximum distance criterion function was obtained. © 2021. All Rights Reserved.

关键词： Feature extraction

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

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Procedia computer Science 2025年 258卷 893-902页

作者： Santosh Kumar Henge Avinash Bhagat Sanjeev Kumar Mandal Nikhil Reddy Viraati Ravleen Singh K. Rushi Kedhar Associate Professor Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal 506371 India Associate Professor School of Computer Applications Lovely Professional University Phagwara Punjab India Assistant Professor Department of CS and IT Jain (Deemed-to-be University) Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States of America Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddewaram AP India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach; it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate.

关键词： Fuzzy Logic Control (FLC) Artificial Neural Network (ANN) Multiple Layered Perceptron (MLP) Convolutional Neural Network (CNN) Feed Forward Neural Network (FNN) Optical Character Recognition (OCR) Handwritten (HW)

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GAN-CNN Ensemble: A Robust Deepfake Detection Model of Social Media Images Using Minimized Catastrophic Forgetting and Generative Replay Technique

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Procedia computer Science 2024年 235卷 948-960页

作者： Preeti Sharma Manoj Kumar Hitesh Kumar Sharma Research Scholar School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun 248007 India Associate Professor Faculty of Engineering and Information Sciences University of Wollongong in Dubai UAE MEU Research Unit Faculty of Information Technology Middle East University Amman 11831 Jordan

Deep-fake photographs are difficult to discern from real ones, especially when utilized in social media platforms. Anyone can willfully create disinformation about public personalities, politicians, and celebrities using these deep fake photographs. So, it is an important need of society to work for an effective model for its detection. The models for deep fake detection commonly use CNN-based detectors. These detectors experience a drop in performance when used for transfer learning or continual learning techniques. A significant limitation in this process is CNN's catastrophe forgetting defect. For the solution of this problem, a Generative replay technique in the form of a GAN-CNN model is implemented that works to minimize this catastrophe forgetting issue that further helps for better detection. It involves generating and storing samples from previous tasks and then replaying them during the training of new tasks which makes CNN more robust to identify deep fakes. The GAN model used in this work is traditional DCGAN improved with necessary adjustments to achieve training stability. It is observed that the model attained a good accuracy of 98.67%(training),70.08% (testing) and minimum loss with a value of 0. 0337 for 100 epochs. Also, it acquired good precision values of 68% and 72%, Recall values are 74% and 66%, and F1 scores of 71% and 69% for classes 0 and 1 respectively. The model outcome is found stable and reliable in deep fake detection under dynamic training conditions. Optimum values of evaluation parameters ensure the model's capacity to learn new tasks preserving the existing task-learning knowledge.

关键词： Deep Learning CNN GAN Catastrophe forgetting CNN continual learning Lifelong learning GAN-CNN deep fake detector

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A novel liver tumor classification using improved probabilistic neural networks with Bayesian optimization

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 8卷

作者： Kolli, Srinivas Parvathala, Balakesava Reddy Krishna, A.V. Praveen Assistant Professor Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad 500090 India Associate Professor Dept of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh India

One of the most common forms of cancer is liver cancer, and successful treatment depends greatly on an early diagnosis. The two primary steps in liver tumor segmentation are liver identification and then tumor segmentation. A sophisticated meta-heuristic technique called Improved Probabilistic Neural Networks and Bayesian Optimization (IPNN-BO) is also introduced to carry out the best classification. In the study, liver computed tomography (CT) scan pictures were gathered for training purposes and added to the LiTs17 dataset. Utilizing performance matrices like accuracy, precision, recall, specificity, sensitivity, and F Measure, the suggested technique is implemented in MATLAB 2021(a). Modern technologies such as KNN, CNN, and DCNN, among others, are used to organize and validate possible policies. The comparison results reveal that the proposed technique outperforms and outperforms the most recent methodologies. IPNN-BO may obtain validation accuracy of up to 99.25 % after applying Bayesian optimization, while KNN, CNN, and DCNN only manage validation accuracy of 88 %, 92.85 %, and 93.75 %, respectively. © 2024 The Authors

关键词： CT images Liver tumor Probabilistic neural network and Bayesian optimization ResUNet model

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A robust protocol to compute wind load coefficients of telecommunication towers and antennas using numerical simulation for risk and resilience assessment

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Resilient Cities and Structures 2024年第1期3卷 66-83页

作者： Mohanad Khazaali Liyang Ma Keivan Rokneddin Matteo Mazzotti Paolo Bocchini Research Engineer Extreme Event Solutions at VeriskLafayette City CenterBostonMA 02111USA Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of TechnologyHarbin 150090PR China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbin 150090PR China Senior Research Scientist RenaissanceRe Risk SciencesUSA Research Associate P.M.Rady Department of Mechanical EngineeringUniversity of Colorado Boulder1111 Engineering Dr UCB 427BoulderCO 80309USA Professor Dept.of Civil and Environmental EngineeringLehigh University117 ATLSS DrBethlehemPA 18015-4729.USA Director Catastrophe Modeling CenterLehigh UniversityBethlehemPAUSA

An accurate estimation of wind loads on telecommunication towers is crucial for design,as well as for perform-ing reliability,resilience,and risk *** particular,drag coefficient and interference factor are the most significant factors for wind load *** tunnel tests and computational fluid dynamics(CFD)are the most appropriate methods to estimate these *** wind tunnel tests are generally preferred in practice,they require dedicated facilities and personnel,and can be expensive if multiple configurations of tower panels and antennas need to be tested under various wind directions(e.g.,fragility curve development for system resilience analysis).This paper provides a simple,robust,and easily accessible CFD protocol with widespread applicability,offering a practical solution in situations where wind tunnel testing is not feasible,such as complex tower configurations or cases where the cost of running experiments for all the tower-antennas configurations is prohibitively *** turbulence models,structural and fluid boundary conditions and mesh types are tested to provide a streamlined CFD modeling strategy that shows good convergence and balances accuracy,computational time,and *** protocol is calibrated and validated with experimental studies available in the *** demonstrate the capabilities of the protocol,three lattice tower panels and antennas with different configurations are analyzed as *** protocol successfully estimates the drag and lateral wind loads and their coefficients under different wind *** differences are observed between the esti-mated wind loads with this protocol and those computed by a simple linear superposition used in most practical applications,indicating the importance of tower-antenna ***,as expected,the wind loads recom-mended by design codes overestimate the simulated *** importantly,the telecommunication design codes inadequate

关键词： Generalized CFD protocol Lattice tower Tower panel Microwave antenna Wind coefficients Tower-antenna interaction

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Deep Reinforcement Learning for Energy Efficiency Optimization using Autonomous Waste Management in Smart Cities

Deep Reinforcement Learning for Energy Efficiency Optimizati...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： T. Kavitha Koushik Reddy Chaganti Sivananda Lahari Reddy Elicherla M. Ravi Kumar D. Chaithanya K Manikanta Assistant Professor Department of CSE Srinivasa Ramanujan Institute of Technology Research Scholar Dept. of Computer and Information Systems University of the Cumberlands Associate Professor Department of CSE Dayananda Sagar University Assistant Professor Department of Cyber Security & IoT Malla Reddy University Assistant Professor Department of CSE CVR College of Engineering Assistant Professor Department of CSE Gates Institute of Technology

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Smart cities face vital waste management challenges regarding efficiency and sustainability in their development period. The established waste collection approaches that depend on pre-set schedules together with unchanging routes create problems by possessing both inefficient resource usage and elevated fuel usage as well as delayed waste pickup times. The proposed framework builds a Deep Reinforcement Learning (DRL)-based system for dynamic waste collection route optimization using Autonomous Waste Collection Vehicles (AWCVs) with Internet of Things (IoT)-based smart bins. The system uses Deep Q-Networks (DQNs) to analyze traffic conditions data alongside vehicle battery level data with smart bin information from the system so vehicles carry out efficient waste collection operations using minimal energy. The newly proposed algorithm provides 30% distance reduction and 28% faster responses and 30% lower energy usage than baseline heuristic methods do. The conclusions show that AI-powered waste management strategies can create sustainable operations which increase both service performance and overall energy efficiency in cities. The research team plans to apply the proposed approach to actual waste collection systems while exploring methods of deploying multiple agents alongside renewable energy systems to enhance future urban waste management operations.

关键词： Waste management Schedules Renewable energy sources Smart cities Deep reinforcement learning Energy efficiency Real-time systems Vehicle dynamics Sustainable development Optimization

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Intrusion Detection in Novel WSN-Leach Dos Attack Dataset using Machine Learning based Boosting Algorithms

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Procedia computer Science 2023年 230卷 90-99页

作者： Dharini N Jeevaa Katiravan Sruthi Priya D M Sakthi Sneghaa V A Associate Professor Department of Computer Science Engineering(Cyber Security) R.M.K. College of Engineering and Technology Thiruvallur 601206 Professor Department of Information Technology Velammal Engineering College Surapet Chennai 600066 Software Engineer embedUR Chennai 600013 Jr. Customer Service Executive Tata Communications Transformation Services Chennai 600037

Security poses a substantial challenge in the context of wireless sensor networks (WSNs). WSNs, which are frequently employed for real-time monitoring in various Internet of Things (IoT)-based applications, are particularly vulnerable to a range of Denial of Service (DoS) attacks. These attacks represent a significant security concern and are among the most prevalent issues confronting WSNs *** our research, we have introduced a WSN DoS dataset using the Matrix Laboratory (MATLAB) tool. This dataset was developed through the utilization of the Leach Protocol and incorporates various types of DoS attacks, including Wormhole, Blackhole, Grayhole, Flooding, and Time Division Multiple Access (TDMA) attacks. In our study, we assessed the performance of several machine learning techniques using our dataset. We employed various performance evaluation metrics that gauge both the classification accuracy and the complexity of these machine learning models. These metrics included parameters such as the number of accurately classified and misclassified instances, along with the time required for constructing the model. This comprehensive evaluation aimed to demonstrate the effectiveness of our proposed *** results of our analysis revealed that the Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Bagging algorithms exhibited strong performance on our dataset. They achieved impressive accuracy levels of up to 99.16%, 99.16%, and 99.8%, respectively, when using 14 features. Additionally, these algorithms achieved accuracy rates of 99.16%, 98.75%, and 99.8%, respectively, with 5 features, and 95.41%, 95%, and 93.75%, respectively, when considering only 3 features such as node ID and Energy-Consumed, Attack_type. Notably, LightGBM outperformed the others in terms of speed, as it employs a leaf-wise growth strategy, consumes less memory, and requires less time for model construction. Consequently, LightGBM emerged as the most efficien

关键词： Denial of Service (DoS) attacks Intrusion Detection System (IDS) Leach Protocol WSN DoS dataset Ensemble Learning Feature Selection Accurately Classified and Misclassified instance Light Gradient Boosting Machine (LightGBM)

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Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture

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computers and Security 2022年 116卷

作者： M .P . , Ramkumar Reddy, P.V. Bhaskar Thirukrishna, J.T. Vidyadhari, Ch. Assistant Professor Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India Professor School of Computer Science and Engineering REVA University Karnataka Bengalore India Associate Professor Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore560082 India Assistant Professor Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

With the rapid expansion of Internet services and increasing intrusion issues, conventional intrusion detection techniques cannot work well with several difficult intrusions. Though several intrusion detection methods have been introduced in the past years, developing an Intrusion Detection Systems (IDS) with prevailing intrusion detection strategy is still very much desirable. Hence, a robust and effective intrusion detection approach, named RV coefficient+Exponential Sea Lion Optimization-enabled Deep Residual Network (ExpSLO-enabled DRN) using spark is devised for the intrusion detection. Here, the unique features are selected using proposed RV coefficient-based hybrid feature fusion, which is designed by the incorporation of wrapper, class-wise information gain (CIG), and Canberra distance in slave node. With the distinctive features selected, the process of data augmentation is done using oversampling for making the data more appropriate to perform the further process in the slave node. Moreover, DRN classifier is utilized for detecting the intrusions in the master node where the DRN training is done using devised ExpSLO algorithm, which is the hybridization of Exponentially Weighted Moving average (EWMA) and Sea Lion Optimization (SLnO). Furthermore, the devised method obtained better performance by considering the evaluation metrics, such as precision, recall, and F-measure with the higher values of 0.8800, 0.8845, and 0.8822 based on without attacks using dataset-2. © 2022

关键词： Intrusion detection

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A Novel Automatic Text Document Classification Using Learning based Text Classification(LbTC) Approach

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Procedia computer Science 2025年 258卷 4279-4290页

作者： Avinash N J Krishnaraj Rao Rama Moorthy H Raviprakash B Raghunadan K R Vasudeva Venkatadri M Department of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidre 574225 Karnataka India Department of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Department of Computer Applications Nitte Institute of Professional Education Nitte (Deemed to be University) Mangalore 575007 Karnataka India Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Associate Dean MPSTME Shirpur Campus NMIMS (Deemed-to-be-University) Shirpur Maharashtra India

There are practical uses for text document classification. In actuality, it is crucial to categorize and parse documents with natural language. Utilizing applications like bogus news identification, query tagging, sentiment classification, and spam filtering needs this sort of study. However, because of their ambiguity, open-ended nature, and vastness, text documents provide a difficult Classification challenge. Machine learning (ML) methods became valuable alongside the growth of artificial intelligence (AI) due to their data-driven learning techniques. These methods are seen as highly effective at handling and analyzing vast data sets in depth. Topic segmentation, text categorization, entity identification, machine translation, and text summarization, to name a few, are just a few of the issues that may be resolved with ML approaches. In this study, we introduced an automatic Text Classification system (ATCF), a system that uses shallow and deep neural networks to classify text documents. To implement our system, we proposed an approach called Learning based Text Classification (LbTC). We investigated the performance of several models in comparison. Based on the training provided to ML models, the suggested framework assists in categorizing any type of document. It is compatible with practical applications where the categorization of documents is essential.

关键词： Machine learning shallow learning deep learning text classification document Classification

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A Novel Intelligent AI-Based Security to Enhance the Data Communication: An Empirical Review

A Novel Intelligent AI-Based Security to Enhance the Data Co...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Shaik, Amjan Hung, Bui Thanh Chakrabarti, Prasun Siva Shankar, S. Parveen, Nikhat Department of Computer Science and Engineering Dean R&D St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh Ho Chi Minh Viet Nam Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Adjunct Distinguished Professor ITM SLS Baroda University Gujarat Vadodara India CSE K G Reddy College of Engineering and Technology Moinabad India CSE Koneru Lakshmaiah Education Foundation AP Guntur Vaddeswaram India

ISBN: (纸本)9789819947164

The transmission of digital data between two devices is termed data communication. As the world nowadays relies on digital communication, the transferring of data among various devices is vitally important, and hence, the security of the data is a concern. Several works are carried out to secure the data transmission between the devices in various applications like mobile communication, wireless communication, vehicular communication, and more. Incorporated with security, the data classification is also performed to ignore the unwanted or hackers’ data. In this work, we have discussed several secure data communication-related papers from various applications. Works including machine learning-based approaches such as decision tree, linear regression, logistic regression, KNN, K-means, and deep learning-based approaches such as a convolutional neural network, recurrent neural network, and long short-term memory approaches were discussed and listed with their limitations along with merits. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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