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AI Based Monitoring System for Social engineering 3

AI Based Monitoring System for Social Engineering

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Yapa, K. Udara, S.W.I. Wijayawardane, U.P.B. Kularatne, K.N.P. Navaratne, N.M.P.P. Dharmaphriya, W. G. V. U Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets;and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits. © 2021 IEEE.

关键词： Social networking (online)

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Navigating Adverse Weather: An Exploration of YOLOv8's Object Detection in Fog 10

Navigating Adverse Weather: An Exploration of YOLOv8's Objec...

引用

10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Haris, Muhammad Iqbal, Mansoor Malik, Muhammad Osama Bokhari, Syed Ahmad Saleem Alahmari, Saad Azam, Farooq Institute for Insight - J Mack Robinson College of Business Georgia State University Georgia United States University of Wah Department of Computer Science Wah Cantt Pakistan University of Tulsa Department of Business Analytics Oklahoma United States Harvard Extension School Harvard University CambridgeMA02138 United States Northern Border University Department of Computer Science Arar Saudi Arabia Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

Object detection in adverse weather conditions, such as dense fog, poses a critical challenge for computer vision systems in applications like autonomous driving, surveillance, and robotics. Image quality degradation due to light scattering and absorption diminishes the performance of even the most advanced models trained on clear-weather datasets. This paper uses the Foggy Cityscapes dataset to evaluate YOLOv8, a cutting-edge object detection model, under extreme foggy conditions. We utilize YOLOv8's sophisticated architecture, featuring the CSPDarknet backbone and Path Aggregation Network (PANet), for efficient feature extraction and multi-scale detection. Our approach incorporates image enhancement techniques and data augmentation to mitigate the effects of fog on feature visibility. We conduct a comprehensive performance assessment using metrics like mean average precision (mAP) precision-recall curves to evaluate the model's robustness and reliability. Additionally, we benchmark YOLOv8 against previous YOLO versions, detailing improvements and trade-offs in detection speed and accuracy. We also propose a hybrid framework combining multi-task learning to improve image quality and object detection in low-visibility scenarios. © 2024 IEEE.

关键词： Light scattering

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Web Accessibility of the Cyprus Island Food Retailers' Websites

Web Accessibility of the Cyprus Island Food Retailers' Websi...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Erkut Inan Iseri Kaan Uyar Umit Ilhan Dept. of Physics Near East University Nicosia Turkey Dept. of Software Engineering Near East University Nicosia Turkey Dept. of Computer Engineering Near East University Nicosia Turkey

Accessibility is the process of making information and electronic communication environments meaningful and usable for most people including those with disabilities. It is not always an easy task to provide all user communities with different areas of interests and needs with exact styles and contents. It is a good practice to employ responsible designs and a certain degree of interaction that provide equitable conditions to suit the needs of the most user communities. It became evident that in the passed two years the world went thru a terrifying Coronavirus disease which made the accessibility of web sites of retailers even more challenging. Most of the food supplying companies introduced shop-to door deliveries of food products with web based applications. Although, technically speaking, these web sites accomplished the task for most of the people but with some accessibility issues. These issues can be identified and solved by following the international standards to gain world wide acceptance. This study covers the investigation of the accessibility of the Food Retailers' websites in the whole of Cyprus Island. Web Content Accessibility Guidelines 2.1 (WCAG 2.1) published by the World Wide Web Consortium (W3C) is followed in the process. Three testing software are used to determine the degree of compliance of the food retailers with the WCAG 2.1 in Cyprus Island. The aim of this study is to raise awareness about web accessibility among general public. The findings are not so promising and the sites examined are not in compliance with the standard of the guidelines defined in WCAG.

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Moving Robots in Unknown Environments Using Potential Field Graphs 2

Moving Robots in Unknown Environments Using Potential Field ...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Panagoda, Mithila Lokuliyanage, Minuli Senarath, Amadi Nisansala, N.K.V.M. Nethmini Rajapakshe, R.W.A.D.U. Rajapaksha, U.U. Samantha Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Deprt. of Computer System and Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

The purpose of this research paper is to introduce a new navigation algorithm for Robot Operating System (ROS) based robots which will allow complete autonomous traversal in any given indoor environment. Turtle bot3 burger bot is the sample robot chosen for this project. This will be equipped with a Light Detection and Ranging (LIDAR) scanner with the default settings, ultrasound, Intel Real Sense camera, etc. Environment information gathered via these sensors will be sent into a remote computer for processing since the raspberry pie micro-controller with the turtle bot burger is not enough to carry out a high computation-intensive task. Once the information is received series of tasks will be carried out by the remote personal computer before executing the newly developed navigation algorithm. The data from the LIDAR scanner help to generate a 2-dimensional(2D) potential field graph. Then it will later be used for path planning by navigation algorithm. Furthermore, the Real sense camera is used for object boundary detection to produce more accurate data for the navigation algorithm. Once all the data proceed, data will return to the robot, where the navigation algorithm will start execution in parallel with a recovery behavior algorithm and scan algorithm. The recovery behavior algorithm is responsible for guiding the robot to face away from an obstacle in an event where the predetermined path is obstructed. Furthermore, it is responsible for maintaining accurate data in the cost map. Subsequently, the scanning algorithm will be used to generate maps. © 2022 IEEE.

关键词： Cameras

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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry 5

Monitoring and Forecasting Quality Indicators of Predough Pr...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Zhyltsov, Andrii Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350384499

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them. ©2024 IEEE.

关键词： Bakeries

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Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Signal Processing Techniques from Accelerometers on Railway ...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Boronakhin, Alexander M. Bolshakova, Alexandra V. Klionskiy, Dmitry M. Larionov, Daniil Yu. Shalymov, Roman V. Saint Petersburg Electrotechnical University 'LETI' Faculty of Information Measurement and Biotechnical Systems Dept. of Laser Measurement and Navigational Systems Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Dept. of Laser Measurement and Navigational Systems Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Dept. of Information Systems Dept. of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350360646

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels' surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities. © 2024 IEEE.

关键词： continuous wavelet transform discrete wavelet transform irregularity of railway track vibration acceleration signal vibration movement wavelet scalogram

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Generative Adversarial Networks for Anomaly Detection: A Systematic Literature Review

Generative Adversarial Networks for Anomaly Detection: A Sys...

引用

International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Shah Noor Sibghat Ullah Bazai Muhammad Imran Ghafoor Shah Marjan Saira Akram Fatima Ali Dept. Computer Science Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Electrical Engineering Superior University Pakistan Television Corporation Lahore Pakistan Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan

In numerous research areas, anomaly identification is a major problem. Identifying and properly classifying data as anomalous is a challenging task that is resolved in various manners over the years. Different approaches like traditional, supervised, unsupervised, and semi-supervised are used for anomaly detection. In the literature, various machine learning-based anomaly detection algorithms exist. It is challenging to choose one anomaly detection algorithm from the several available algorithms because each algorithm puts forward its good detection performance. In recent years, generative adversarial networks have shown remarkable results for anomaly classification. This paper aims to represent a systematic literature review of generative adversarial network-based approaches for anomaly detection and highlights their pros.

关键词： Data privacy Machine learning algorithms Systematics Bibliographies Machine learning Generative adversarial networks Mathematics

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Rare and Common Types of Retinal Disease Recognition Using Ensemble Deep Learning

Rare and Common Types of Retinal Disease Recognition Using E...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Al Shahriar Uddin Khondakar Pranta Arafath Bin Mohiuddin Amira Hossain Anamul Sakib Md. Moniruzzaman Shanto Shamim Forhad Abdullah Al-Sakib Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Computer Science Wright State University Dayton USA Department of Collage of Engineering and Technology Westcliff University CA USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Detecting retinal diseases is crucial for preventing vision loss, but it comes with challenges, such as data imbalance and accurately classifying rare conditions. This study introduces a new ensemble transfer learning model that combines EfficientNetB7, SE-ResNet, DenseNet-169, and NASNet to improve the classification of common and rare retinal diseases. The ensemble uses a probability averaging approach to maximise each architecture’s strengths. Essential preprocessing techniques such as Global Contrast Normalization (GCN) and Total Variation Denoising (TVD) were used to enhance image quality. The model was trained and evaluated on the Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0, achieving 98.65% accuracy and an F1-score of 0.96% for common diseases and 99.10% accuracy with an F1-score of 0.97 for rare diseases, outperforming individual models significantly. The ensemble has been integrated into a web-based application that allows clinicians to upload retinal images for instant diagnostic feedback, enabling real-time analysis and visual insights. Despite its strengths, challenges such as high computational demands and reliance on high-resolution photos remain. Future work will improve computational efficiency through model compression techniques and extend the model to other imaging modalities, such as Optical Coherence Tomography (OCT). In conclusion, this study presents a robust and scalable solution for retinal disease classification, potentially improving early detection and providing real-time support in clinical settings, particularly in resource-limited environments.

关键词： Deep learning Visualization Accuracy Computational modeling Optical coherence tomography Transfer learning Imaging Retina Real-time systems Diseases

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Sudha Merlin K. Malathy Ravi S Arun M Raut S.P H. Fawareh Department of Electrical and Electronics Engineering DMI College of Engineering Palanchur Chennai Department of MCA S.A. Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Dept of Computer Science and Engineering Kings College of Engineering Punalkulam Thanjavur Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of consumer data can be attributed to “the Internet of Things (IoT),” which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics.

关键词： Analytical models Consumer behavior Accuracy Computational modeling Neural networks Predictive models Data models Internet of Things Logic Business

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Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset 15

Network Intrusion Detection System Using Deep Learning Metho...

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Tanimu, Jesse Jeremiah Hamada, Mohamed Robert, Patience Mahendran, Anand Bayero University Kano Dept. of Computer Science Kano Nigeria University of Aizu Software Engineering Lab. Aizu Japan Federal Polytechnic Bali Department of Computer Science Bali Nigeria Higher School of Economics Theoretical Computer Science Lab. Moscow Russia

ISBN: (纸本)9781665464994

This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation function to arrive at a better performance. Results based on the KDDCUP'99 dataset show that our approach provides significant performance improvements over other deep sparse autoencoder Network Intrusion Detection Systems. © 2022 IEEE.

关键词： Intrusion detection

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