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Advancements in Computing (ICAC), International Conference on

作者： Dharsan. R Krishanthini M Traveena C Anubama L Mahaadikara M.D.J.T. Hansika Sanjeevi Shahikala Chandrasiri Department of Computer Science and Software Engineering (CSSE) Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Law enforcement agencies and police departments face challenges in analyzing and preventing accidents and crimes. This research presents a comprehensive machine learning system, “The Future of Crime Prevention: Police Case Analysis,” to enhance public safety. The system comprises components like accident case analysis, crime analysis, case document classification, and crimes against women analysis. It leverages various machine learning techniques to predict accident causes, analyze criminal cases, automate document classification, and cluster crimes against women. The objectives encompass bolstering public safety, enabling proactive decision-making, anticipating future crime rates, and optimizing document analysis. This research strives to provide effective tools for crime and accident prevention, addressing traditional challenges faced by police departments and crime divisions. In an era of advancing technology, the need for innovative solutions to ensure public safety has never been more crucial. Furthermore, it seeks to bridge the gap between law enforcement agencies and cutting-edge technology, revolutionizing the landscape of crime prevention and public safety.

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A Framework for Tele-rehabilitation Gaming System 6

A Framework for Tele-rehabilitation Gaming System

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Elaklouk, Ahmed M. S. Zuarni Ramli, Ratna Edris, Ibrahim Padilla-Valdez, Nena Jumaily, Adel Al Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Mara Negeri College of Computing Informatics and Mathematics Seremban Malaysia Universiti Teknologi Brunei Centre for Communication Bandar Seri Begawan Brunei Faculty of Engineering University of Technology Brunei Brunei Department of Computer Science and Software Engineering School of Computing University of Western Australia Australia Ensta Bretagne University France

ISBN: (纸本)9798350317428

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study's objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient's motivation and engagement and increase the effectiveness of rehabilitation. © 2023 IEEE.

关键词： Serious games

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems

Robust and Reliable Liveness Detection Models for Facial Rec...

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Frontiers of Information technology (FIT)

作者： Haris Anjum Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Abu Bakar Siddique Muhammad Imad Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model’s accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system.

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Fatima Faridoon Raja Hashim Ali Zain Ul Abideen Nazia Shahzadi Ali Zeeshan Ijaz Usama Arshad Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis.

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning

Exploiting Partial Observability and Optimized Simple State ...

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Frontiers of Information technology (FIT)

作者： Danyaal Mahmood Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent’s performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent’s decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game.

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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Intelligent Emergency Communications Using Efficient Clustering Based Routing Strategy in Unmanned Aerial Vehicle Networks

Intelligent Emergency Communications Using Efficient Cluster...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： K. Nagamani V. Sujatha M Praveena Abla Suliman Hussein R Adakane Suresh D Department of MBA QIS College of Engineering and Technology Ongole AP Department of Master of Computer Applications S.A. Engineering College Chennai Tamilnadu India Department of CSE QIS College of Engineering and Technology Ongole Andhrapradesh Dept of Software Engineering Faculty of Information Technology Zarqa University Jordan University of Business and Technology Jeddah SA Department of ME Yeshwantrao Chavan College of Engineering (YCCE) Nagpur India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

The IECECR model is designed to use a network of Unmanned Aerial Vehicles (UAVs) to provide emergency communications in disaster areas where existing infrastructure is not accessible. UAVs are deployed in clusters with cluster heads (CHs) selected based on trust and mobility metrics to ensure stable and efficient data transmission to ground stations. It also allows intra- and inter-cluster for short and long-distance communication. Changes in environmental conditions through dynamic clustering led to improved network reliability and longevity. The analysis outlines the system motivation, considering infrastructure loss scenarios, and provides a system model that includes mini-drones as well as medium-sized UAVs with 3D navigation capabilities. The proposed cluster protocol integrates location-aware technologies and wireless communication functions, which shows its applicability in emergencies and disaster management. The implementation is performed in NS3 and the parameters which are considered for result calculations are packet delivery ratio, energy efficiency, data loss, routing overhead, and average delay are used to compare CMAC, TCDC, and VSNC with the IECECR model. It shows our proposed model enhances overhead and efficiency.

关键词： Wireless communication Disasters Disaster management Autonomous aerial vehicles Routing Energy efficiency Data models Delays Reliability Vehicle dynamics

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Fingerprint Matching for Noisy and Distorted Patterns Using a Siamese Network With ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Kumar Lehana, Parveen Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii Ali Alzubi, Ahmad University of Jammu DSP Laboratory Department of Electronics Jammu180006 India University of Jammu Department of Computer Science and IT Jammu180006 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab Rajpura140401 India Manipal University Jaipur Department of Computer Science & Communication Engineering Jaipur303007 India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa42-200 Poland Faculty of Informatics and Software Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv03056 Ukraine King Saud University Community College Department of Computer Science Riyadh11451 Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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A Fault Forecasting Approach Using Two-Dimensional Optimization

A Fault Forecasting Approach Using Two-Dimensional Optimizat...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Mohsen Tajgardan Reza Khoshkangini Abbas Orand Mahdi Rabbani Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Computer Science and Media Technology Internet of Things and People (IOTAP) Malmö University Malmo Sweden Arriver Software AB Qualcomm Company Linköping Sweden Canadian Institute for Cybersecurity University of New Brunswick NB Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Data preparation is crucial in every machine learning approach, particularly when applied to detecting claims in the automotive industry. The challenge of managing highdimensional feature spaces and imbalanced data becomes even more pronounced with the proliferation of IoT devices, which generate vast amounts of data over time. Machine learning models trained on such imbalanced datasets often yield unreliable and inaccurate predictions. Thus, addressing these issues during the data pre-processing phase is critical. In this paper, we introduce a novel two-dimensional optimization (TDO) strategy to tackle the problem of imbalanced data in fault detection, specifically in the context of IoT-enhanced automotive systems. We leverage a heuristic optimization technique known as the Genetic Algorithm to simultaneously reduce both the number of data point tuples and the feature space. Additionally, we evaluate the effectiveness of two-dimensional reduction using Particle Swarm Optimization (PSO) and Whale Optimization algorithms. Our empirical results, derived from data collected from thousands of IoT-equipped vehicles, demonstrate the promise of our proposed methods.

关键词： Industries Fault detection Machine learning Predictive models Whale optimization algorithms Internet of Things Particle swarm optimization Optimization Automotive engineering Genetic algorithms

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Cluster Availability Model with Information Update Following Node Recovery

Cluster Availability Model with Information Update Following...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： V. A. Bogatyrev S. V. Bogatyrev V. V. Sivov Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia Faculty of Software Engineering and Computer Technology ITMO University Saint-Petersburg Russia

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. Information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. Information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node.

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