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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Malakar, Madhuri Mahapatro, Judhistir National Institute of Technology Rourkela Department of Computer Science and Engineering Odisha Rourkela769008 India

ISBN: (纸本)9798331531195

This paper delves into the existing routing schemes in Vehicular Ad hoc Networks (VANETs), critically examines their limitations, and proposes an efficient routing scheme for Software-Defined Vehicular Networks (SDVN). The proposed scheme employs a grouping mechanism to establish a robust path between the source and destination vehicles by using velocity vectors and cartesian coordinates of vehicles. Additionally, the scheme incorporates real-time vehicle information, such as GPS location, direction, speed, and vehicle density, and the Expected Transmission Count (ETX), as a link-quality routing metric for superior link quality. The ETX metric ensures the selection of better and more stable links, while the grouping mechanism identifies links with a high probability of breakage prior to their occurrence for efficient routing. The real-time link-quality information between the vehicles is dynamically generated and monitored at the control plane, ensuring up-to-date and accurate routing decisions. We evaluate the performance of the proposed scheme against traditional approaches. Simulation results show that the proposed scheme outperforms the existing schemes, achieving higher throughput, lower packet loss, reduced end-to-end delay, and significantly fewer path breaks with the network scenario of increasing vehicle density and speed. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Evaluating Ensemble Models on Imbalanced Data Sets: A Comparative Study across Varied Minority Class Ratios 4

Evaluating Ensemble Models on Imbalanced Data Sets: A Compar...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Surajit Nayak, Samaleswari P. Sahoo, Biswajit Nayak, Sarat Chandra Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Silicon Institute of Technology Dept. of Computer Science and Engineering Bhubaneswar India Gitam Deemed to Be University School of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349856

The complexities of using ensemble models to rectify class imbalance in healthcare datasets are investigated in this study. Using well-known methods such as Random Forest, Gradient Boosting, AdaBoost, CatBoost, LightGBM, XGBoost, BRF and EE we perform an extensive analysis on data-sets that exhibit different levels of class imbalance. The study uses stratified k-fold cross-validation for robust evaluation and the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation. ROC curves are shown, and performance indicators like as accuracy, precision, recall, F1-score, and area under the curve (AUC) are examined. The results shed light on the complex effects of ensemble models on unbalanced healthcare data-sets and offer directions for further investigation. © 2024 IEEE.

关键词： Adaptive boosting

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Advance Path Loss Model for Distance Estimation Using LoRaWAN Network's Received Signal Strength Indicator (RSSI)

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IEEE Access 2024年 12卷 83205-83216页

作者： Vo, Hoang Hoang Long Nguyen, Van Tran, Van Lic Ferrero, Fabien Lee, Fang-Yi Tsai, Meng-Hsun The University of Danang-University of Science and Technology Faculty of Advance Science and Technology Da Nang550000 Viet Nam The University of Danang-University of Science and Technology Department of Electronic and Telecommunication Engineering Da Nang550000 Viet Nam LEAT CNRS Universite Cote d'Azur Sophia Antipolis06903 France National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications. © 2013 IEEE.

关键词： Kalman filters

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Anticipating the Nearness of Coronary Heart Infection Utilizing Machine Learning Classifiers 2

Anticipating the Nearness of Coronary Heart Infection Utiliz...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Salem Akoosh, Lamiaa Mohammed Siddiqui, Farheen Zafar, Sherin Naaz, Sameena Alam, M Afshar Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

Researchers are putting in a lot of time and energy into developing methods to use machine learning algorithms, a subfield of AI, to diagnose disease in an individual patient. Extensive studies have been conducted on the potential benefits of using machine learning techniques in the treatment of cardiovascular disease. For this preliminary study, the study have zeroed down on heart illness to get as specific as possible about our methodology. In this paper, the authors investigate the differences in accuracy between different machine learning approaches specifically, (SVM), (KNN), and a(ANN) when applied to the categorization of cardiovascular illness. Our research makes use of approximately seventy thousand patient records from the Kaggle dataset, which focuses on cardiovascular disease The Kaggle dataset comprises a limited number of variables per patient record, encompassing serum cholesterol, diastolic and systolic blood pressure, relative blood glucose levels, and the presence or absence of angina. The present study examines the Kaggle dataset and employs the (KNN), (SVM), and (ANN) methodologies. The results indicate that the K-nearest neighbors (KNN) algorithm, specifically with a value of 9 for the number of neighbors, achieved an accuracy of ?0.997. The SVM model using default hyperparameters achieved an accuracy of 0.9997. The Support Vector Machine (SVM) model utilizing a Radial Basis Function (RBF) kernel and a C value of 100.0 exhibited an accuracy of 0.9998. Conversely, the SVM model employing a linear kernel and a C value of 1000.0 achieved a perfect accuracy of 1.000. The Feedforward neural network achieved a perfect accuracy of 1.000 using the Adam optimization algorithm after 10 epochs and 50 *** future research, the authors intend to employ a deep neural network hypermodel in order to enhance the accuracy of their findings. © 2024 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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SPECKLE NOISE DETECTION AND REMOVING BY MACHINE LEARNING ALGORITHMS IN MULTISENSORY IMAGES FOR 5G TRANSMISSION

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Scalable Computing 2024年第4期25卷 2679-2686页

作者： DHARANI, M. NAGENDRANATH, M.V.V.S. RAFEE, SHAIK MOHAMMAD KISHORE, G.NAVEEN VENKATAKRISHNAMOORTHY, T. Department of Electronics and Communiation Engineering Mohan Babu University Andhra Pradesh Tirupathi India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of AIML Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Electronics and Communication Engineering Andhra Pradesh Tadepalligudem India Department of Electronics and Communication Engineering Sasi Institute of Technology & Engineering AP Tadepalligudem India

The multispectral satellite sensor images have multibands, which have some typical noise. There is difficult to detect this tipical noise with low resolution image. The satellite local or gloval pixel information and quantificationare degraded due to this noise. Many standard transformations and filtering operations are developed for detection and removing of non-gaussion noise, which are not given sophisticated results with existing methods. These statistical characteristics are applied to those samples to identify and quantify present tipical noise. The higher-order statistical based machine learning algorithm is developing to remove the speckle noise from satellite image. In this proposed algorithm, implemented the higher order statistical approache such as skewness, kurtosis based adaptive curvelet methods are implemented for the detection and suppression of speckle noise with retrieve of spectral and spectral values. The proposed algorithm preserves smooth and sharp details and maintains the tradeoff level in multispectral bands is suitable for advanced high speed 5G communication with the effective rate of transmission. The proposed results are verified with suitable statistical parameters such as PSNR,Entropy and ERGAS values. © 2024 SCPE.

关键词： Learning algorithms

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Early and Automated Diagnosis of Dysgraphia Using Machine Learning Approach

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SN computer science 2023年第5期4卷 523页

作者： Agarwal, Basant Jain, Sonal Beladiya, Kishan Gupta, Yash Yadav, Avijit Singh Ahuja, Neelu Jyothi Department of Computer Science and Engineering Central University of Rajasthan Ajmer India PG Department of Computer Science and Technology Sardar Patel University Vallabh Vidhyanagar India Department of Computer Science and Engineering Indian Institute of Information Technology Kota Kota India Department of Computer Science School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India

Dysgraphia is a handwriting problem that impairs a person’s ability to write. Even the diagnosis of this condition is challenging, and there is currently no cure. Researchers from all over the world have studied this issue and offered several solutions. Motivation to work on this problem did arise after meeting with a few students struggling in achieving performance despite putting in sincere efforts. This paper also discusses the various forms of dysgraphia and its associated symptoms and proposes machine-learning models to detect dysgraphia. Unsupervised machine learning techniques are used to detect dysgraphia-related handwriting impairment. To accomplish the goal, a fresh handwriting dataset is created by conducting handwriting exercises and a wide variety of features are extracted to represent various handwriting characteristics. Results indicate that Random forest returns the best accuracy but scores less while detecting dysgraphic samples correctly. One class SVM has been tried to deal with the issue of the availability of dysgraphic samples required to train machines. Results indicate good hope in identification with a scope of improvement with increase in sample size for machine training. This paper also seeks to raise awareness of the dysgraphia issue and its effects on society. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Dysgraphia Learning difficulties Machine Motor ability OCC One class SVM Random forest

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Despeckling SAR Images Using CNN-Based Approach Incorporating GAN and Gradient Estimation 27th

Despeckling SAR Images Using CNN-Based Approach Incorporatin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Saha, Anirban Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna801106 India

ISBN: (纸本)9783031783111

Synthetic Aperture Radar (SAR) technology stands at the forefront of capturing and processing Earth’s surface visuals due to its widespread acceptance across various organizations. However, the presence of unwanted random granular interference, commonly referred to as "speckle," poses a significant challenge in SAR data processing. Addressing this challenge, known as "despeckling," is crucial for extracting clear SAR visuals. This article introduces a novel CNN-based approach for despeckling SAR visuals contaminated with speckle. Our proposed model integrates a Generative Adversarial Network (GAN) module to estimate the distribution of contaminating speckle components from the input SAR data. Concurrently, a gradient estimator module captures the crisp changes in textural information within the input data. Subsequently, the input SAR data, the estimated noise distribution, and the extracted gradient undergo further processing through a deep convolutional module to generate a clean SAR visual. Unlike traditional methods that focus solely on learning the residual noisy component or the clean data, our proposed despeckling model learns the degradation pattern caused by noisy components while emphasizing gradient information, thereby capturing critical minute information. Experimental results demonstrate that our methodology significantly enhances despeckling performance compared to existing technologies in the literature. This research presents a promising step forward in advancing SAR visual despeckling techniques, with implications for improved data quality and interpretation in various applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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A New Approach to Design Smart Surveillance System by Enhancing Doorbell Mechanism 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Dwivedi, Shashank Tiwari, Amit Kumar Sankalp, Prem Gupta, Pradeep Shubbham Malviya, Abhishek Department of Computer Science and Engineering United Institute of Technology Uttar Pradesh Prayagraj211008 India

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

Smart Doorbell and Locking system is a system of interconnected IoT devices and sensors. This is an IOT device for home or office automation. Traditional doorbells serve the purpose but this smart doorbell can save plenty of time with the help of face recognition module. The purpose of this project is not just limited in saving time but it also offers some safety features. This device is just a step towards smart homes. Although there are plenty of commercial smart doorbells available but they are quite expensive and some of them lacks features that this project offers. This device can connect to Wi-Fi and can be accessed with mobile devices which is time-saving and provides complete control of this device through fingertips. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition IOT OpenCV Raspberry Pi Smart doorbell Smart lock

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Security challenges of IoT with its applications and architecture

Security challenges of IoT with its applications and archite...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Bathla, Neha Kaur, Amanpreet Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9781032669663

IoT (Internet of Things) is the network of physical objects("things") in which embedded devices are used like sensors and software that are used for connecting the devices and objects over the internet. Currently, seven billion connected IoT devices today, by 2025 experts is expecting this number to grow to twenty-two billion. In this article we provide an introduction or overview of the IoT (Internet of Things), IoT architecture and functionality of each layer, its applications and security issues. The IoT are enabled by the newest developments in smart sensors and RFID. The basic principle of IoT is that it has smart sensors that work together directly without human participation to deliver the new applications. In the upcoming years, with the intelligent decision making IoT expected as bridge diverse technologies to enable new applications by connecting physical objects together. In this survey we also describe the importance of security and its challenges. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Internet of things

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