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Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Intelligent Automation & Soft Computing 2023年第3期35卷 3349-3364页

作者： P.Pandi Chandran N.Hema Rajini M.Jeyakarthic Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of CSE Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630003India Department of Computer and Information Science Annamalai UniversityChidambaram608002India

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily *** Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading *** recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify *** this motivation,this study focuses on the design of mayﬂy optimization with a deep belief network for PDF malware detection and classiﬁcation(MFODBN-MDC)*** major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the *** proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature *** addition,Adamax optimizer with the DBN model is used for PDF malware detection and classiﬁ*** design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-ﬁcation demonstrates the novelty of the *** demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various *** comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

关键词： PDF malware data classiﬁcation security deep learning feature selection metaheuristics

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Cyber-physical energy system simulation tool: framework for network co-simulation, GridLAB-D and ns-3

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International Journal of Simulation and Process Modelling 2022年第4期18卷 314-328页

作者： Singh, Brijendra Pratap Gore, Manoj Madhava School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad India

A cyber-physical energy system consists of information and communication technology, advanced power electronic devices, energy sources, and smart appliances. A simulator is required for research and to further enhancements in the cyber-physical energy system. The integration of a power system and information and communication technology raises the problem of integration of time-driven activity and event-driven activity. The power system simulators are time-driven, while the network communication simulators are event-driven. The Pacific Northwest National Laboratory, Richland, Washington developed a tool named ‘framework for network co-simulation (FNCS)’ which co-simulates ns-3 (a network simulator) and GridLAB-D (a power distribution system simulator). This work details the enhancement of the FNCS application program (which is integrated into ns-3) to incorporate the functionalities, namely: 1) internal real-time price calculation;2) day-ahead charging schedule of a battery. The enhancement has been used for the development of cyber-physical application programs, namely, internal real-time pricing and battery charging schedules. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Application programs

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Enhancing Security in the Internet of Things: A Trust-Based Protocol for Resilient Communication

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SN computer science 2024年第1期5卷 4页

作者： Althaf Ali, A. Hussain, Mohammed Muzaffar Subramaneswara Rao, A. Lavanya, S. Feroz Khan, A.B. Department of Computer Applications Madanapalle Institute of Technology & Science (MITS) Andhra Pradesh Madanapalle India Department of Computer Science and Engineering C. Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India Department of Software Applications Thiruthangal Nadar College Tamil Nadu Chennai India Department of Computer Science Syed Hameedha Arts and Science College Tamil Nadu Kilakarai India

The quick increasing in the Internet of Things (IoT) devices has raised significant security concerns, particularly in the face of reactive jamming attacks. This paper proposes a trust-based protocol named Trust-Based Protocol for Resilient Communication (TBRC) that incorporates a robust key management technique to improve the security of IoT systems and mitigate the impact of reactive jamming attacks. To assess the effectiveness of our protocol, the work is compared with existing schemes such as CKMT, EDDK, and CEMT. Through extensive simulations under realistic conditions, we analyze key performance metrics, including energy consumption, packet loss, computational time, and throughput. The comparative analysis demonstrates that our proposed protocol outperforms CKMT, EDDK, and CEMT in terms of computational efficiency, throughput, and delay, making it a more effective solution for secure IoT communication. By leveraging a robust key management technique, our protocol provides a secure and resilient communication framework for IoT environments, effectively countering reactive jamming attacks. The integration of this technique strengthens the overall security posture of IoT networks, ensuring reliable and secure communication between devices. This work contributes to advancing IoT security by introducing a trust-based protocol with a robust key management technique that effectively mitigates the impact of reactive jamming attacks. The comparative evaluation against existing schemes validates the superiority of our proposed approach in terms of security, efficiency, and reliability. The results of our evaluation highlight the improved performance of our protocol, positioning it as a promising solution for real-world IoT deployments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DDoS IoT Security Trusted metrics

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Dynamic generative R-CNN

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Neural Computing and Applications 2025年第10期37卷 7107-7120页

作者： Saffarini, Rasha Khamayseh, Faisal Awwad, Yousef Sabha, Muath Eleyan, Derar IT Engineering Arab American University Jenin Palestine Department of Computer Science Palestine Polytechnic University Hebron Palestine Department of Computer System Engineering Palestine Technical University Tulkarm Palestine Department of Multimedia Technology Arab American University Jenin Palestine Department of Applied Computing Palestine Technical University Tulkarm Palestine

Different techniques have been developed for object detection and recognition. These techniques can be divided into single-shot and two-shot methods. Single-shot methods focus on real-time applications, while two-shot methods are used in applications requiring higher accuracy. However, different versions of the two-shot techniques produce limited results in terms of accuracy and speed, or both. Therefore, this study proposes a novel model called dynamic generative R-CNN (DGR-CNN) that reduces the number of proposed regions using a dynamic programming model that applies the graph similarity method over graph-based image segmentation. Additionally, the proposed model employs DCGAN technique to improve detection performance. DGR-CNN reduces the overall detection and classification time and enhances the detection accuracy. The PASCAL VOC2007 and MS COCO datasets were utilized to evaluate the model. The results showed that DGR-CNN significantly reduces the number of candidate regions compared to the selective search algorithm employed in R-CNN and fast R-CNN. Although fast R-CNN utilizes 2000 regions and faster R-CNN utilizes 300 regions, DGR-CNN reduces the number of regions to approximately 130. The mean average precision of the proposed method was 75.1% on the PASCAL VOC2007, while fast and faster R-CNN scored 66.9% and 69.9%, respectively. Moreover, the DGR-CNN model significantly improved the classification accuracy when tested on the MS COCO dataset, achieving an MAP of 68.76%, compared with 32.64% and 42.3% for fast and faster R-CNN. This increase in accuracy was achieved without significantly compromising the speed compared with faster R-CNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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VitaFALL: Advanced Multi-Threshold Based Reliable Fall Detection System

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Recent Advances in computer science and Communications 2022年第1期15卷 32-39页

作者： Patel, Warish D. Patel, Chirag Patel, Monal Department of Computer Science Engineering and Technology Parul University Vadodara India Department of Computer Science BITS Edu Campus Vadodara India

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for signifi-cant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. Therefore, even for the daily activity in the life of aged people, an automatic fall detecting system and vital signs examining system have become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people Methods: In comparison with present algorithms, there are various benefits regarding privacy, suc-cess rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algo-rithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%. ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS mod-ules so that the aged people can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The results of the experiment show the ade-quacy of the proposed approach. © 2022 Bentham science Publishers.

关键词： Fall detection

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Pathological Analysis of Blood Cells Using Deep Learning Techniques

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Recent Advances in computer science and Communications 2022年第3期15卷 397-403页

作者： Ranga, Virender Gupta, Shivam Agrawal, Priyansh Meena, Jyoti Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

Introduction: Pathologists are majorly concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. However, this method has a number of limitations such as there is no standard way of diagnosis, there are certain chances of human errors and besides, it burdenizes the laboratory personnel to diagnose a large number of slides on a daily basis. Methods: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to get an insight into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When an input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are applied in accordance with the proposed algorithm, then the model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, and testing loss for the proposed model is plotted using matplotlib and trends. Discussion: The performance of the proposed model is better than the existing standard architectures and other works done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory personnel. . This can also in turn help the pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyp

关键词： Diagnosis

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Automated Breast Cancer Diagnosis Using Deep Learning CNN Models 2

Automated Breast Cancer Diagnosis Using Deep Learning CNN Mo...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Saravanan, G. Jeevanantham Prabin, S.M. Parthasarathy, R. VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331534950

The study introduces a deep learning model, CCNN, developed to identify and classify eight types of breast cancer: normal adenosis, normal fibroadenoma, normal phyllodes tumor, normal tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. This model was integrated with five pre-existing models - Xception, InceptionV3, VGG19, MobileNetV2, and ResNet101 - all trained for image classification using the ImageNet database, to categorize breast cancer MRI images. The dataset, sourced from Kaggle's repository, was further augmented using the Generative Adversarial Network (GAN) technique. For 30 experiments carried out to evaluate the introduced model and the transfer learning of five and proposed CCNN models, the F1-score values accuracy yielded were respectively: 98.04%, 96.13%, 98.54%, 97.11%, 94.20% and 98.98%. This study emphasizes the importance of improving the accuracy of early breast cancer detection to enhance the survival rates of patients. © 2024 IEEE.

关键词： Generative adversarial networks

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Teleoperation in the Age of Mixed Reality: VR, AR, and ROS Integration for Human-Robot Direct Interaction 4

Teleoperation in the Age of Mixed Reality: VR, AR, and ROS I...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Nandhini, P. Chellammal, P. Jaslin, J.S. Harthy Ruby Priya, S. Uma, M. Kaviyaraj, R. Sri Shanmugha College of Engineering and Technology Department of Computer Science and Engineering Sankari India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350300093

Robotic systems that can be operated remotely are becoming more and more common in a variety of industries, including construction, disaster relief, space exploration, and industrial applications. Teleoperation, however, necessitates skilled operators who can handle sophisticated data processing and programming. This research introduces a unique teleoperation method that makes use of Virtual Reality (VR) and the Robot Operating System (ROS) to overcome this problem. By creating a 3D representation of the robot's workspace in Unity and connecting it to the actual robot with ROS, this technique optimizes control. Programming is not necessary because the robot's vision serves as the foundation for the user interface. Through ROS integration, a target point is established in Unity and sent to the robot as the end effector position. By simply setting up a new environment, reducing the requirement for knowledgeable operators, and improving situational awareness, this method may be used to any sort of robot. For even greater situational awareness, this technique may also be used with augmented reality (AR). AR can give the operator real-time information about the robot's surroundings by superimposing digital information over the physical world. This can be achieved by including AR markers in the physical surroundings that the VR system can detect. In the operator's field of view, the system can then superimpose information such as sensor data or robot status onto the real-world environment. © 2023 IEEE.

关键词： Augmented reality

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Parkinson's Disease Detection using Deep Learning 15

Parkinson's Disease Detection using Deep Learning

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Anandaraj, B. Monisha, P. Krishna Vinaya, M. Laya, V. Mamatha, V. Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Annamayya-Dt Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331300579

Parkinson's disease is a common neurological condition that predominantly im-pacts people who are older than fifty, leading to speech impairments and movement diffi-culties. Timely diagnosis of PD is essential for efficient care and improved patient outcomes, especially considering the global aging population. Our paper introduces a new method for the early identification of Parkinson's disease utilizing machine learning techniques and the Xception architecture. Our focus lies on analyzing spiral and wave drawings, commonly used in clinical diagnosis. We curated a dataset comprising such drawings from individuals with and without PD and processed the data for model training. Leveraging the Xception architecture, our machine learning models achieved promising results, with a training accu-racy of 95.34% for spiral drawings and 93.34% for wave drawings, along with validation accuracies of 93.00% and 86.00%, respectively. Our highlight the capabilities of machine learning methodologies and the Xception architecture in improving the precision and accu-racy of the diagnosis of Parkinson's disease. Implementing our proposed approach could significantly advance early detection efforts, thereby enhancing patient care and quality of life. © Grenze Scientific Society, 2024.

关键词： Neurodegenerative diseases

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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