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2023 International Conference on Decision Aid sciences and Applications, DASA 2023

作者： Bernine, Nassima Nacer, Hassina Adel-Aissanou, Karima Aissani, Djamil University of Bejaia LaMOS Research Unit Operational Research Department Algeria University of Science and Technology 'USTHB' Movep Laboratory Computer Science Department Algiers Algeria University of Bejaia LaMOS Research Unit Computer Science Department Algeria

ISBN: (纸本)9798350342055

This paper defines analytical models for a Web services system, with two stations on tandem queue, for evaluating the request satisfaction involving two exponential servers, each having its own waiting queue. The first waiting queue is unlimited. The arrivals of requests follow a Poisson process, and after service completion, they can either leave the system or join the second line. The obtained model has not a product form;and the only way to obtain its performances is by computing its steady-state probabilities. To this end, we must use balance equations that give us unlimited equations with unlimited unknowns. In this case the only solution is the use of approximation methods. In this paper we use the truncation method to approximate the performances of our model with the performances of a truncated model. We also use the strong stability method to estimate the error of this approximation. Furthermore, we compute the system's response time, and the average number of clients in the system in terms of the arrival rate of clients requests. © 2023 IEEE.

关键词： Websites

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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Intelligent waste classification approach based on improved multi-layered convolutional neural network

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Multimedia Tools and Applications 2024年第36期83卷 84095-84120页

作者： Chhabra, Megha Sharan, Bhagwati Elbarachi, May Kumar, Manoj Department of Computer Science & amp Engineering School of Engineering & ampTechnology Sharda University UP Gr. Noida India School of Engineering and Sciences Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati India Head of School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman11831 Jordan

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%. © The Author(s) 2024.

关键词： Convolutional neural networks

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution

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Soft Computing 2024年第17-18期28卷 9317-9339页

作者： Saini, Shubham Patel, Jyoti Garg, Renu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India Machine Intelligence Unit Indian Statistical Institute Kolkata700108 India Department of Statistics Ramanujan College Delhi110019 India

In this article, we draw inferences on stress–strength reliability in a multicomponent system with non-identical strength components based on the progressively censored data from the Kumaraswamy distribution (KuD). When one shape parameter of KuD is known, the uniformly minimum variance unbiased estimator is produced. To evaluate the reliability of such systems when all the parameters are unknown, the maximum likelihood and Bayes estimators are developed. Along with coverage probabilities, the asymptotic confidence and highest posterior credible (HPD) intervals are also obtained. Tierney–Kadane’s approximation and Markov chain Monte Carlo methods are used for Bayesian computations. To compare the performance of estimators, a Monte Carlo simulation study is performed. Also, one real-life example is analyzed for illustrative purposes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Maximum likelihood estimation

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Fault Secured JPEG-Codec Hardware Accelerator with Piracy Detective Control using Secure Fingerprint Template 36

Fault Secured JPEG-Codec Hardware Accelerator with Piracy De...

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36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2023

作者： Chaurasia, Rahul Reddy Asireddy, Abhinav Sengupta, Anirban Indian Institute of Technology Computer Science and Engineering Indore India Indian Institute of Technology Department of Computer Science and Engineering Indore India Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India

ISBN: (纸本)9798350315004

This paper presents a novel methodology to design fault secured JPEG-compression-decompression (codec) hardware accelerator with piracy detective control using secure fingerprint template of intellectual property (IP) vendor. In the proposed approach, firstly, the fault secured schedule for JPEG-codec design is generated from its behavioral description. Subsequently, in order to enable detective control against piracy, secure digital template corresponding to fingerprint biometric of IP vendor is generated and implanted into the design. In order to do so, firstly, fingerprint features are transformed based on IP vendor specified key-set. Subsequently, the generated secure fingerprint template in the form of encoded hardware security constraints is implanted into the design during behavioral/ high-level synthesis phase of integrated circuit (IC) design. The implanted hardware security constraints thereby ensure the seamless detection and isolation of fake/pirated design versions during piracy detection process, before their conjunction into system on chip (SoC) of electronic systems. The proposed approach achieves the following: i) fault secured hardware accelerator for JPEG-codec with integrated piracy detective control at negligible design cost overhead ii) higher resiliency against tampering attack and iii) lower probability of coincidence (indicating definitive proof of ownership for genuine IP vendor). © 2023 IEEE.

关键词： Fault detection

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A Comprehensive Approach of Algorithmic Trading System based on Technical Analysis

A Comprehensive Approach of Algorithmic Trading System based...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Jongpermwattanapol, Apisan Jongkohpo, Khathathorn Worasucheep, Chukiat Hiruncharoenvate, Chaya Krongtong, Tassanee King Mongkut's University Of Technology Engineering Science Classroom Bangkok Thailand King Mongkut's University Of Technology Applied Computer Science Bangkok Thailand Securities And Exchange Commission Bangkok Thailand The Institute For The Promotion Of Teaching Science The Institute And Technology Technology Unit Bangkok Thailand

ISBN: (纸本)9798350370096

Machine Learning researchers have been attempting to accurately predict stock price trends. Moreover, financial researchers use Technical Analysis with optimization models [1] to achieve the same task by extracting and combining important features for the model. Despite many Algorithmic Trading systems (ATS) available, modelling the stock market has still been proven difficult since stock prices are nonlinear and often appear random [2]. This work focuses on leveraging the advantages of both by employing the Technical Analysis approach to create features for training Machine Learning models. The models were evaluated using data from nine stocks listed on the Stock Exchange of Thailand (SET) from 2019 to 2023. The performances of three machine learning models: Logistic Regression, K-Nearest Neighbors, and Support Vector Machines were compared with Differential Evolution and the buy-and-hold strategies. The results showed that the Differential Evolution can significantly improve the return on investment in comparison to the Machine Learning models and the buy-and-hold strategies. © 2024 IEEE.

关键词： Financial markets

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Personalized Differential Privacy Preservation Method for Trajectory Based on Regional Density Analysis 2

Personalized Differential Privacy Preservation Method for Tr...

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2nd International Conference on Big Data, Information and computer Network, BDICN 2023

作者： Zhi, Weicheng Gong, Xiaoze Wang, Yongli Nanjing University of Technology School of Computer Science and Engineering Nanjing China Unit 63850 of PLA Baicheng China

ISBN: (纸本)9798350321364

As a reliable privacy protection method, differential privacy has been widely used in trajectory data release. However, the current trajectory protection method based on differential privacy lacks the analysis of the privacy requirements of each user and allocates the same privacy budget to all trajectory location points of the user, which cannot provide personalized trajectory privacy protection according to user characteristics. Aiming at this problem, we propose a trajectory protection method based on area density analysis. Analyze the stay area of each user, calculate the stay point and reconstruct the trajectory set according to the time and distance thresholds. Using the minimum spanning tree clustering algorithm based on local density peaks to obtain the privacy-sensitive location points and active hotspot areas of the user's trajectory. According to the designed privacy importance degree expression, calculate privacy score of each sensitive location point, and assign them the proper privacy budget value. The experimental comparison on real data sets shows that this trajectory privacy protection method can better reduce the privacy budget waste and improve the availability of data compared with the traditional differential privacy trajectory protection method. © 2023 IEEE.

关键词： Trajectories

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Using Hybrid Genetic Algorithm for Data Aggregation in Wireless Sensor Networks 18

Using Hybrid Genetic Algorithm for Data Aggregation in Wirel...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Sharmin, Sharmin Ahmedy, Ismail Noor, Rafidah Md Ismail, Habibah Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Computer Science Unit Centre of Foundation Studies Universiti Teknologi MARA Selangor Dengkil43800 Malaysia

ISBN: (纸本)9798350331011

Efficient energy usage is vital for extending Wireless Sensor Networks (WSNs) lifespan. While Improved LowEnergy Adaptive Clustering Hierarchy (ILEACH) excels in energy-efficient data aggregation, challenges like premature cluster head (CH) failure remain. Genetic Algorithm (GA) optimizes parameters, including energy, in WSNs. We propose a novel hybrid ILEACH-GA algorithm for data aggregation. ILEACH forms clusters, GA evaluates fitness, selecting optimal clusters for aggregation. GA mitigates ILEACH's premature CH failure. ILEACH-GA surpasses LEACH, ILEACH, and GA-LEACH, with significantly higher throughput (10.0%, 47.4%, 21.9 respectively), retaining higher residual energy (0.0805) and alive nodes (25.5%). This innovation boosts sustainable WSN data aggregation, overcoming limitations, and enhancing performance. This innovation elevates sustainable WSN data aggregation, surmounting limitations, and augmenting performance, applicable in waste and crop management systems. © 2024 IEEE.

关键词： Energy utilization

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information Communication technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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