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12th International Conference on Software and computer Applications, ICSCA 2023

作者： Nakanishi, Tsuneo Ototake, Hokuto Tanabe, Toshifumi Yoshimura, Kenji Department of Electronics Engineering and Computer Science Faculty of Engineering Fukuoka University Fukuoka Japan

ISBN: (纸本)9781450398589

The quality of development documents written in natural language is basically ensured by developer’s review with quite a few man-hours. Natural language processing (NLP) tools can partially automate the time-consuming review. The authors have developed morpheme level pattern matching and processing tools morfgrep and morfawk for Japanese texts for this purpose;moreover, in this paper, extends them to perform pattern matching on dependency among phrases. Example applications of the tools, proofreading of development documents and domain-specific term extraction, are also presented. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Pattern matching

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AI-Enhanced Virtual Twin Modelling for Strengthening IoT Software Security Protocols 1

AI-Enhanced Virtual Twin Modelling for Strengthening IoT Sof...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Kumara, Bharath Barmola, Pareshwar Prasad Arora, Renuka Parikh, Swapnil M Maranan, Ramya Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Parul Institute of Engineering and Technology Parul University Faculty of Computer Science and Engineering Vadodara India Simats Saveetha School of Engineering Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

To fortify security mechanisms in software systems for the Internet of Things (IoT), this article presents a framework for AI-Enhanced Virtual Twin Modelling. In order to track and examine the actions of IoT devices in real-time, the suggested method makes use of virtual twin technology that is combined with machine learning techniques. Automated response generation, continuous threat assessment, and anomaly detection are made possible by creating a digital clone of the actual IoT network using the virtual twin paradigm. By combining deep learning models like CNNs and LSTMs, it becomes easier to forecast possible security risks in network traffic data by seeing intricate spatial and temporal patterns. When tested in a virtual Internet of Things (IoT) setting, the suggested framework outperforms conventional rule-based approaches by a wide margin, reducing reaction time by 35% while achieving an accuracy of 9 6. 2 %in threat identification. © 2024 IEEE.

关键词： Risk assessment

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Modelling an Efficient URL Phishing Detection Approach Based on a Dense Network Model

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computer Systems science & engineering 2023年第11期47卷 2625-2641页

作者： A.Aldo Tenis R.Santhosh Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatoreTamil NaduIndia

The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called *** deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator(URLs)*** legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions,which deals with the detection of ***,the URLs in both classes from the login page due,considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having *** addition,some model reduces the accuracy rather than training the base model and testing the latest *** addition,a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign.A new dataset called the MUPD dataset is used for ***,a prediction model,the Dense forward-backwards Long Short Term Memory(LSTM)model(d−FBLSTM),is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5%on the initiated login URL dataset.

关键词： Cyber-attack URL phishing attack attention model prediction accuracy

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Development of a Nail-based Application for the Detection of Disease using Image Processing 2

Development of a Nail-based Application for the Detection of...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gote, Pradnyawant M. Kumar, Praveen Dhale, Trishul Mishra, Gaurav Vedprakash Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Jawaharlal Nehru Medical College Department of Radiodiagnosis Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Disease diagnosis using non-invasive techniques is a key determinant in the improvement of outcomes in patients and the enhancement of precautionary health measures. The study focuses on the design and development of a nail-based detection application employing image processing techniques to detect diseases such as systemic disease, fungal disease, Leukonychia (White spots on nails) and Paronychia (Nail bed infection) through analysis of the patterns of the fingernails and other related symptoms. The developed system integrates several techniques, comprising a customized imaging application, a user-friendly interface, and software for real-time image processing and disease classification. A variety of algorithms proposed for image processing, such as Median filtering, Sobel Mask and HOG features, are designed to extract important features from nail images and then use machine learning models that match these features against a database of established markers of various diseases. Comprehensive testing utilizing an annotated dataset of nail images demonstrated the system's accuracy in classifying certain diseases, further showcasing its potential for possible usage as a proficient tool for early disease diagnosis. Thus, this application is easy to use, user-friendly interface, and provides advancements in diagnosing diseases both in clinical settings and in the comfort of one's home. The incorporation of this technology into routine clinical practice might facilitate the possibility of early diagnosis and early intervention, which could improve patient outcomes and reduce healthcare system burdens. © 2024 IEEE.

关键词： Median filters

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer's Patients 2

Integrating Decision Trees with Cloud Computing for Predicti...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

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Hybrid arithmetic optimization algorithm with deep transfer learning based microarray gene expression classification model

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3923-3928页

作者： Gowri, B. Shyamala Nair, S. Anu H. Kumar, K. P. Sanal Department of Computer Science and Engineering Annamalai University Annamalai Nagar Tamil Nadu Chidambaram 608002 India Department of Computer Science and Engineering Faculty of Engineering and Technology Deputed To WPT Annamalai University Chennai India Department of CS RV Government Arts College Chengalpattu India

Microarray gene expression (MGE) data classification is a vital challenge in biomedical and genomics research, designed to understand the difficult relations among genes and numerous biological states. In this procedure, high-dimensional gene expression data is primarily pre-processed and converted to create it responsive to machine learning (ML) techniques. Feature selection and dimensionality reduction models are frequently used to remove the most appropriate gene signatures. medicine, and drug improvement, making it a vital element of recent genomics studies. Therefore, this manuscript presents a hybrid arithmetic optimization algorithm with a deep transfer learning-based MGE data classification (HAOADTL-MGEDC) model. The HAOADTL-MGEDC technique initially undergoes the conversion of gene expression data into image format. Then, the preprocessing of the images takes place using the median filtering-based noise removal and contrast enhancement approach. Besides, the feature extraction procedure is implemented by the usage of the capsule network (CapsNet) model. Meanwhile, the CapsNet hyperparameters can be optimally selected by the use of HAOA. Finally, the parallel bidirectional gated recurrent unit (BiGRU) methodology has been used for the classification of GE data. The experimental values of the HAOADTL-MGEDC technique have been tested on a benchmark dataset. Extensive simulation analysis inferred the higher of the HAOADTL-MGEDC technique compared to other DL approaches. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep Transfer Learning Gene Expression Hyperparameter Tuning Median Filtering Microarray

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Application of Machine Learning for Detection and Prediction of Malaria: A Review 3

Application of Machine Learning for Detection and Prediction...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Apetorgbor, Mawuli Wankhede, Vidhi Dakhare, Kajal Verma, Prateek Raut, Yudhishthir Tripathi, Chanchla Department of Science and Technology Maharashtra Wardha India Faculty of Engineering and Technology Maharashtra Wardha India Pimpri Chichwad University Department of Computer Science and Engineering Maval Sate Pune India Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350381931

Mosquitoes carries a parasitic infection that affects millions of peoples globally. The initial and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all considerably diminish the consequences of malaria. Machine learning is a promising method to tackle this problem with its capability to unveil and understand complex patterns from broad and multifaceted dataset. Accuracy of drug dosage and disease management in healthcare industry has proven phenomenal accuracy with use of machine learning algorithm. The current analysis explores the application of machine learning for prediction of malaria cases. This paper showcase the inclusion of numerous data source, comprising environmental factors, medical history and epidemiologic information to demonstrate how machine learning influences this to abundance of data to enhance prediction accuracy. This study also enhances the major advancements in prior malaria detection that have been made feasible and practicable by machine learning driven symptomatic technologies such prompt analytical test and image analysis of blood taints. Concerns like model comprehensibility, data secrecy, and equitable distribution of healthcare assets are also concealed in this review study. © 2024 IEEE.

关键词： Forecasting

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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Implementing Microservice Architecture for An Elevator Monitoring System to Improve Scalability and Flexibility 18

Implementing Microservice Architecture for An Elevator Monit...

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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Arifuddin, Lukman Nurba, Hardy Purnama Sarief, Ivany Setiawan, Asep Trisna Munastha, Ketut Abimanyu Lestari, Nina Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Universitas Bandar Lampung Faculty of Computer Science Lampung Indonesia

ISBN: (纸本)9798331527686

The demand for more efficient and reliable elevator monitoring systems has increased due to security and safety issues in high-mobility buildings. This paper proposes a microservice architecture for an elevator monitoring system to enhance scalability, flexibility, and fault tolerance by decomposing the system into more minor, loosely coupled services. This paper will discuss the proposed system's design, implementation, and evaluation. The system, equipped with ESP32Cam and ultrasonic sensors, excels in real-time monitoring. Data is processed and analyzed using Python and OpenCV, allowing the system to not only count the number of people in the elevator but also detect any anomalies, ensuring the safety of the occupants. LED notification alerts, managed by NodeMCU8266, are triggered based on ultrasonic sensor data. MongoDB handles user management and analytical data, while the dashboard offers a user-friendly interface for real-time monitoring and system management provide essential information for elevator operators. The architecture's use of MQTT ensures efficient communication between sensors and services, creating a robust, scalable, and efficient solution. © 2024 IEEE.

关键词： Ultrasonic sensors

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Improving Dynamic Behavior of Vehicular Ad Hoc Networks by Integrating Game Theory Technique 5th

Improving Dynamic Behavior of Vehicular Ad Hoc Networks by I...

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5th International Conference on Applied Technologies, ICAT 2023

作者： Tiwari, Shobhit Mani Baghel, Anurag Singh Department of Computer Science and Engineering Faculty of Engineering and Technology University of Lucknow Uttar Pradesh Lucknow India Department of Computer Science and Engineering School of ICT Gautam Buddha University Uttar Pradesh Greater Noida India

ISBN: (纸本)9783031589553

In this paper, we give the focus on the continuous advancement in the domain of Vehicular ad hoc networks (VANET’s) and that is developed as a tool for developing the base for platform intelligent mode in the communication (inter vehicle communications) and needful for enhanced improvement under the performance effect and traffic safety. Here, we have deployed the game theory method in the VANET domain and thereafter categorizes between the hop vehicle and the source vehicle. The New approach to the VANET analysis we have identified and used the social parameters and hence made it look more novel approach. "Researching vehicle ad hoc networks poses challenges due to their dynamic nature, the movement of large vehicles, unlimited energy resources, and the evolution of wireless networks. Game theory is commonly employed in wireless networks to explore the interplay between competition and cooperation. In this study, we present a system for ad hoc networks in cars, utilizing game theory to automate car groups and board elections. This approach eliminates the necessity for regular bulk updates. Furthermore, the social behavior of each car is leveraged to create clusters in the car environment. The K-means algorithm in machine learning is applied to develop social cars. The proposed system underwent testing for various characteristics, including CH lifetime, average group lifetime, average number of joins, throughput, and packet loss rate. The results demonstrate that VANET performs exceptionally well, achieving an overall performance ranging from 0.95 to 0.989". © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Heterogeneous networks

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