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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Dommati, Manikanta Kiliroor, Cinu C Department of Computer Science Indian Institute of Information Technology Kottayam Kerala Kottayam India

ISBN: (纸本)9789819717231

Along with the rise of face recognition systems, there is a growing concern over the vulnerability of these systems to spoofing attacks, where an attacker can present a fake or a manipulated face image to bypass the authentication process. Regular updates in the anti-spoofing systems are required to stop attacks on the recognition systems. The existing systems use convolution neural networks as binary classifiers to differentiate between a real face and a spoofed face. We propose a face anti-spoofing system that aims to differentiate between a live and spoofed face image by employing a two levels of verification and. The proposed system is designed to work with RGB cameras and does not require any 3D camera hardware or stereo vision cameras, making it accessible for more practical applications. The proposed system uses a transfer learning technique to perform first level of verification and validate in the second level using computer vision. computer vision aims to build a high-security model for cutting out all the attacks on the facial recognition system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Using Brightness Distribution in Fundus Images to Automated Glaucoma Detection 3

Using Brightness Distribution in Fundus Images to Automated ...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Ahmed, Zakia Lawgali, Ahmed University of Omar Al-Mukhtar Faculty of Sciences Department of Computer Science Albayda Libya University of Benghazi Faculty of Information Technology Department of Computer Science Benghazi Libya

ISBN: (纸本)9798350319897

Glaucoma is a condition of the optic system that can cause vision loss as a result of increased intraocular pressure. The disease is becoming more prevalent, and its diagnosis can be both costly and time-consuming. One prominent area of the retina affected by glaucoma is the optic nerve head, which undergoes structural changes. The goal of the study was to detect glaucoma by analyzing the brightness distribution in optic nerve head fundus images, with statistical parameters serving as the primary means of measurement. Specifically, the mean and standard deviation were extracted as features from the fundus images, representing the central tendency and variation of data within the images. The proposed approach demonstrated highly accurate results, outperforming existing literature in the field. © 2023 IEEE.

关键词： Diagnosis

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Sentimental Analysis for Trump Tweets Using R 8th

Sentimental Analysis for Trump Tweets Using R

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8th International Conference on information system Design and Intelligent Applications, ISDIA 2024

作者： Nishok, V.G. Bharathi, N. Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9789819786657

Twitter is the best source for sentiment analysis, product reviews, current issues, etc. Sentiment analysis extracts positive and negative opinions from the Twitter data set, and R studio provides the best environment for this Twitter sentiment analysis. Find if Trump’s phone Android is fake or not by comparing his iPhone and Android tweets using R with datasets. Detailed analysis is not there. The Twitter data collected might not be representative of the entire population. By using filtering of tweets as we need Android and iPhone tweets here and usage of tweets per day, no of tweets with and without quotes and pictures and links, taking common words from Android and iPhone and trying with log ratio that is Android /iPhone and with log ratio and common word find sentiment. We got good results by the usage of Twitter of each device, no of tweets quoted and non-quoted, no picture/link and picture/link, most common words, words compare with Android and iPhone and log ratio, finding sentiment of it which type of word it is. My work helps his account is a trump or not for Android as it contains negative tweets by common words, Android and iPhone words and log odd ratio with that it detects sentiment as it is a positive, negative or neutral by showing anger, sad, happy, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, and YOLOv9: A Study on Performance and Explainability

Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, a...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Subair, Rubiya Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798331540821

Elephant emotion detection has marked a crucial component owing to the increased casualties due to human-elephant conflicts. The difficulty has increased due to the lack of algorithms that quickly identify emotions with explainable features. This paper proposes a holistic architecture that detects the emotions of elephants using YOLOv5, YOLOv8, and YOLOv9 models, The study aims to integrate explainable AI features such as Eigen-CAM to manifest the training and testing capabilities of models. In addition, we evaluated the capability of the proposed system using labeled datasets that distinguish elephant emotions to angry, happy, and sad. Our experimental results, conducted at the IoT Cloud Research laboratory, revealed training accuracy of 79.3% for YOLOv5, 79.5% for YOLOv8, and 81.1 % for YOLOv9 and test accuracy of 93%, 95%, and 95% for YOLOv5, YOLOv8, and YOLOv9. Additionally, we recorded the impact of resolutions while performing model evaluations. The observations learned from the experiments will benefit AI-assisted machines to detect elephant emotions that can be utilized by mahouts or people living near forest locations. © 2024 IEEE.

关键词： Elephant Emotion Explainable AI Yolo V5 Yolo V8 Yolo V9

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Mitigating denial of service attacks in fog-based wireless sensor networks using machine learning techniques

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Journal of Data, information and Management 2023年第4期5卷 207-225页

作者： Abidoye, Ademola P. Obagbuwa, Ibidun C. Azeez, Nureni A. Department of Computer Science and Information Technology Clayton State University Morrow GA United States Department of Computer Science and Information Technology Sol Plaatje University Kimberley South Africa Department of Computer Sciences University of Lagos Lagos Nigeria

Wireless sensor networks are considered to be among the most significant and innovative technologies in the twenty-first century due to their wide range of industrial applications. Sensor nodes in this networks are susceptible to a variety of assaults due to their special qualities and method of deployment. In WSNs, denial of service attacks are common attacks in sensor networks. It is difficult to design a detection and prevention system that would effectively reduce the impact of these attacks on WSNs. In order to identify assaults on WSNs, this study suggests using two machine learning models: decision trees and XGBoost. The WSNs dataset was the subject of extensive tests to identify denial of service attacks. The experimental findings demonstrate that the XGBoost model, when applied to the entire dataset, has a higher true positive rate (98.3%) than the Decision tree approach (97.3%) and a lower false positive rate (1.7%) than the Decision tree technique (2.7%). Like this, with selected dataset assaults, the XGBoost approach has a higher true positive rate (99.01%) than the Decision tree technique (97.50%) and a lower false positive rate (0.99%) than the Decision tree technique (2.50%). © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2023.

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Artificial Bee Colony with Cuckoo Search for Solving Service Composition

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Intelligent Automation & Soft Computing 2023年第3期35卷 3385-3402页

作者： Fadl Dahan Abdulelah Alwabel Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulﬁll compli-cated user demands according to speciﬁc Quality of Services(QoS)*** user requirements are formulated as a workﬂow consisting of a set of ***,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard *** work will introduce a hybrid Artiﬁcial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition *** ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of ***,the ABC suffers from a slow convergence ***,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local *** proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition *** addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s *** results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestﬁtness value,averageﬁtness value,and average execution time)overall datasets in 30 different ***,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

关键词： Cloud computing web service composition artiﬁcial bee colony cuckoo search

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Real-Time Incremental Federated Learning for Weather Prediction with UAV based Data Collection 18

Real-Time Incremental Federated Learning for Weather Predict...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Singhal, Priyansh Hazra, Abhishek Joshi, Piyush Indian Institute of Information Technology Sri City Department of Computer Science and Engineering India

ISBN: (纸本)9798350387278

This article presents a comprehensive framework for real-time incremental federated learning aimed at enhancing weather prediction accuracy through UAV-based data collection. In our proposed system, UAVs are deployed across multiple regions within cities to gather diverse and comprehensive weather data. The collected data is streamed in real-time to central city servers, which aggregate the data and transmit it to a state-level server. At the state server, data from multiple cities are combined, and models are incrementally trained on newly received data in a step-wise manner. Each incremental step involves training on 80% of the new data and testing on the remaining 20%. Our key contribution is the integration of a weighted federated learning approach, where periodic federated learning steps utilize the weighted FedAvg algorithm. The weights are determined based on the accuracy of each model, allowing for more effective aggregation of model updates. Experimental results demonstrate a significant improvement in prediction accuracy, achieving a 24% increase compared to baseline independent machine learning models trained separately at the state level. © 2024 IEEE.

关键词： Federated learning

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A Novel Framework for Securing information in Cyber Security system using Authentication, Intrusion Detection and Deep Learning-based Risk Prediction Tasks 5

A Novel Framework for Securing Information in Cyber Security...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Naik, R. Lakshman Jain, Sourabh Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

ISBN: (纸本)9798350361155

The cyber security has become a hot research topic nowadays. The term cyber security is frequently employed replaceable with the term data security. In recent days, each system faces the cyber security threat virtually and the model framework should have the capacity to combine the security features and work as the system's integral elements. Various works have been conducted to discover the answer to the evaluation challenge of cyber security and numerous architectures have been designed. However, the techniques face distinct issues though it is functioning well initially at the development time. Hence, an efficient and advanced cyber security framework is implemented in this paper to prevent cyber attacks using deep learning. Initially, to predict the risks in cyber security devices, the necessary data is fetched from the available resources. The gathered data is forwarded to the authentication operation and then the authenticated data is given to the intrusion detection stage. After that, the intrusion-detected data is subjected to the risk prediction stage, where the Weighted Recurrent Neural Network (WRNN) is supported to forecast the risk level of the network. Here, the Dung Beetle Optimizer (DBO) is utilized to tune the weights optimally in WRNN. This authentication, intrusion detection, and risk prediction operations have enriched the security of the cyber networks. Lastly, the efficacy and the security of the implemented model are analyzed with its related models by utilizing several performance metrics. © 2024 IEEE.

关键词： Forecasting

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Penetration Testing-Aware IDS for Wireless Networks in Urban Digital Ecosystems Integrated with Edge Computing and Machine Learning

Penetration Testing-Aware IDS for Wireless Networks in Urban...

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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Adamova, Aigul Satybaldina, Dina L.N. Gumilyov Eurasian National University Faculty of Information Technology Astana Kazakhstan Hazara University Department of Computer Science and Information Technology Mansehra21300 Pakistan

ISBN: (纸本)9798331542603

This research study introduces a new concept of Penetration Testing-Aware Intrusion Detection system (IDS) for wireless networks in urban digital environments. By integrating edge computing and machine learning with the proposed framework, it improves the security controls specific to the threats in urban wireless environments. An analysis was conducted using the UNSW-NB15 dataset for testing and validating two machine learning models of logistic regression and gradient boosting to assess their accuracy in real-time network intrusion detection. Comparing the results obtained in applying the gradient boosting and logistic regression, the first model described an accuracy of 95%, a precision-recall value of 0.97, and an AUC score of 0.94, whereas the second model described an accuracy of 88%, a precision-recall value of 0.92, and an AUC score of 0.81. The IDS also proactively passed a thorough vulnerability analysis and penetration testing and proved the possibility of the identification of a high percentage of attack scenarios with low false positives. The results of the present research highlight the importance of using machine learning approaches coupled with active vulnerability scanning to enhance the protection of wireless networks in urban digital ecosystems. © 2024 IEEE.

关键词： Adversarial machine learning

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