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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Sachi, Savya Singh, Ajay Kumar Jain, Arpit Devi, Suman Sharma, Yogesh Kumar Athithan, Senthil Lalit Narayan Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur842001 India Allenhouse Institute of Technology Uttar Pradesh Kanpur India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. Guntur India Chaudhary Devi Lal University Department of Computer Science and Engineering Haryana Sirsa India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering AP Guntur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering A.P. Vaddeswaram India

ISBN: (纸本)9798350335569

Hate speech on social media has increased as people use it to communicate and express their thoughts. Automatic hate speech detection has been studied in several languages. This study investigates the conclusion of the performance evaluation of the machine learning model GPT-2 (Generative Pre-trained Transformation 2) for natural language processing. The proposed model is developed and evaluated using a hate speech dataset. We attained an accuracy of about 97.12 percent, higher than the existing models called biLSTM, CNN, CNN-LSTM, and BERT. © 2023 IEEE.

关键词： Speech recognition

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Adaptive Control and Simulation of Production Processes Using Temporal Neuro-Fuzzy Petri Net 7

Adaptive Control and Simulation of Production Processes Usin...

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7th International Conference on Information technologies in Engineering education, Inforino 2024

作者： Bobryakov, A.V. Borisov, V.V. Misnik, A.E. Prokopenko, S.A. National Research University 'MPEI' Department of Computer Engineering Smolensk Russia National Research University 'MPEI' Department of Management and Computer Science Moscow Russia Inter-State Educational Institution of Higher Education 'Belarusian-Russian University' Information Techmology Software Mogilev Belarus

ISBN: (纸本)9798350371369

Complex systems surround people in many industries. In the process of functioning, complex systems need to overcome various difficulties associated with the influence of external factors, peculiarities of the functioning of processes, etc. Complex system is influenced by a complex and rapidly changing set of external conditions, where effective planning and optimization of resources become key success factors, while the possible trajectories of the production of complex technical objects, the employment plan of equipment and personnel, which helps to optimize processes and reduce production costs. The article examines one of the classes of complex systems, and also proposes a method for adaptive control of systems of this class using temporal neuro-fuzzy Petri nets. These networks combine the concepts of Petri nets, a graphical system modeling and analysis tool, with neural networks and fuzzy logic. Considered algorithmic and software for the implementation and support of processes. The proposed method makes it possible to increase the efficiency of a complex system under changing operating conditions. caused by the influence of various factors. © 2024 IEEE.

关键词： Complex networks

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A Deep Learning Approach for Detecting Malware Using Autoencoder

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第8期51卷 1051-1059页

作者： Panchagnula, Venu Madhav Satya Keerthi, N.V.L.Ch. Surekha, S. Sujatha, R. Veeraiah, Duggineni Ramesh, Eluri Lakshmi, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India AIML Department Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Information Technology Department M. Kumarasamy College of Engineering Karur India Computer Science and Engineering Department Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Computer Science and Engineering Department R.V.R & J. C. College of Engineering Guntur India Electronics and Communication Engineering Department Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh Nuzvid India

Research in the field of malware detection is currently leaning toward methods based on artificial intelligence algorithms due to the increasing limitations of traditional detection methods and the increasing accuracy of these methods. This study therefore introduces a novel approach to malware detection. This method integrates a deep learning model's autoencoder network with a grayscale graphic depiction of malware. By looking at the autoencoder's reconstruction error, we may assess if the grey-scale picture approach is feasible to use for malware detection. Additionally, it differentiates between malicious and safe software by utilizing the autoencoder's dimensionality reduction capabilities. Using the Android dataset, our newly introduced detection model attained a 95% accuracy and maintained a consistently high F-score of approximately 95%. This outperformed more conventional methods of machine learning detection. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Malware

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An Improved Particle Swarm Approach for Energy-Aware Location-Aided Routing in Mobile Ad-Hoc Network 7

An Improved Particle Swarm Approach for Energy-Aware Locatio...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, Sandeep Sharma, Khemraj Ashok Kumar, P.M. Jain, Arpit Bhagat, Sukesh Kumar Singh, Pranita Tula's institute U.K Dehradun India Nanhua University Doctoral Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering AP Vaddeswaram522502 India Uttaranchal University Department of Computer Science & Engineering Uttarakhand Dehradun India Roorkee Institute of Technology Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798350350067

A network with no infrastructure, multiple hops, and dispersed nodes called a MANET. Nodes can communicate directly or through intermediaries without the need for permanent infrastructure. In dynamic networks like MANETs, routing procedures enhance the overall appearance of the network by ensuring Quality of Service (QoS). A routing protocol may use a composite routing measure to determine the best path for transferring data. Optimal location-based routing for MANETs based on energy-awareness is presented in this paper. Optimal routes are selected using this fast algorithm. Particle swarm optimization (PSO) is used instead of location-aided routing to select the normal data transmission route. Last but not least, the network is tested by simulating its packet delivery ratio, its energy level, its delay, and its overhead. © 2024 IEEE.

关键词： Adversarial machine learning

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Object-oriented Representation of Mechanical Systems for the Automated Design

Object-oriented Representation of Mechanical Systems for the...

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2021 International Conference on Intelligent technologies, CONIT 2021

作者： Lavrik, Vladimir Alieksieieva, Hanna Bardus, Iryna Shchetynina, Olha Berdyansk State Pedagogical University Department of Computer Technologies in Management and Education and Computer Science Berdyansk Ukraine

ISBN: (纸本)9781728185835

At the decision of practical task in the technique of presentation of 2-D and 3-D objects there is a problem of choice of optimum calculation chart which is based on a number of concrete methods of formal design. We developed authoring system of computer-aided design, that is based on special language of presentation of geometrical primitives, that allows a design engineer to create reliable models in his subject domain, and it differs from the traditional methods of presentation of objects in mechanics and construction. The modeling language was presented as a formal system, that allows to formulate and formally prove its qualities. © 2021 IEEE.

关键词： Modeling languages

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement 5

Development of Advanced Noise Filtering Techniques for Medic...

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5th International Conference on Intelligent Communication technologies and Virtual Mobile Networks, ICICV 2024

作者： Kushwaha, Sumit Amuthachenthiru, K. Geetha, K. Narasimharao, Jonnadula Dileep Kumar, M. Gadde, Sai Sudha University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603 203 India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad501401 India Hensard University Faculty of Management Sciences Bayelsa 561101 Nigeria Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Vaddeswaram500075 India

ISBN: (纸本)9798350385649

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality. © 2024 IEEE.

关键词： Medical imaging

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Enhanced Keypoint-Based Approach for Identifying Copy-Move Forgery in Digital Images 7

Enhanced Keypoint-Based Approach for Identifying Copy-Move F...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bisht, Girish Singh Jain, Arpit Bansla, Vandna Sharma, Khemraj Bhutia, Rudramani Kumar, Vikas Tula's Institute Department of Computer Science & Engineering Dehradun248001 India Koneru Lakshamaiah Education Foundation Department of Computer Science & Engineering A.P. Vadessahwaram India Shivalik Engineering College Dehradun248001 India Nanhua University Doctoral Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. India Dev Bhoomi Uttrakhand University School Engineering and Computing Uttrakhand Dehradun India

ISBN: (纸本)9798350350067

Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of 89.2%, 93.9% and 98.98%.) © 2024 IEEE.

关键词： Image analysis

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Effective Seizure Recognition from EEG Data by Asymmetrical Back Propagation Neural Network (ABPN) Classification 4

Effective Seizure Recognition from EEG Data by Asymmetrical ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Gupta, Prateek Mishra, Dinesh Kumhar, Bhupchand Shukla, Prashant Kumar Naveen, K.B. Roy, Lipika Shri Ram Institute of Science and Technology Department of Computer Science and Engineering Madhya Pradesh Jabalpur India Mangalyatan University-Jabalpur Department of Computer Science and Engineering MP Jabalpur483001 India Ies University Ies Institute of Technology and Management Department of Computer Science and Engineering Madhya Pradesh Bhopal462044 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur522302 India Adichunchanagiri University Bgs Institute of Technology Department of Electronics and Communication Engineering India Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

ISBN: (纸本)9798331518981

A significant task within neurology and epilepsy management is the differentiation of seizures from the Electroencephalogram (EEG signal). The current work of research focuses on suggesting an idea to enhance the detection of seizures by applying the Asymmetrical Back Propagation Neural Network (ABPN) classification. This dynamic aspect of ABPN is ideal for learning the details of the features and patterns of EEG since seizures are usually unilateral. We also compare our ABPN classifier with the standard neural networks to confirm that our proposed classifier perform better in sensitivity, specificity, and the overall assessment of the classifier in the global environment. According to the experiment, the authors are convinced that the ABPN model can solve the problem of lowering the false-positive rate along with the increase in the speed of detection to make it suitable for real-time seizure observation. This research will be useful in the improvement of better equipment to detect the seizures with a positive implication on patient health. © 2024 IEEE.

关键词： Neural networks

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil Engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

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computers, Materials & Continua 2024年第11期81卷 2357-2394页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Razaz Waheeb Attar Farman Ali Ahmed Alhomoud Babar Shah School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia College of Technological Innovation Zayed UniversityDubai19282United Arab Emirates

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control *** these networks,nodes function as senders,receivers,and *** such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely *** the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power *** optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in *** paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in *** energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient *** link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of *** simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing *** evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

关键词： FANET UAV internet of UAVs relay mechanism energy consumption routing IEE-DLSA

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