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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Geetha, B. Malathi, P. Thirumalaikumari, T. Janakiraman, V. Basha, H. Anwer Rukmani Devi, S. Saveetha Institute of Medical and Technical Sciences Science Saveetha College of Liberal Arts and Sciences Department of Computer Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Phishing attacks remain a serious problem in cybersecurity, necessitating advances in detection methods. The study describes a novel method for detecting phishing attacks that use advanced machine learning (ML) techniques, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs and RNNs were chosen because of their ability to detect spatial and sequential patterns in phishing data. The system achieves improved performance by iterative model refinement and ensemble learning, beating existing rule-based systems in terms of precision (96%), recall (94%), and F1-score (95%). Furthermore, the proposed strategy has significantly lower false positive (3.8%) and false negative (5.7%) rates than existing systems, indicating greater dependability in distinguishing between legitimate and malicious emails. Cross-validation validates that the proposed system is stable and ready for real-world use. As a result, the proposed ML-driven strategy represents a substantial leap in phishing detection, providing increased accuracy (F1-score: 95%) and resilience to emerging cybersecurity threats. © 2024 IEEE.

关键词： Phishing

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AUTODEEPSLICE: A data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Chowdhury, Deepraj Das, Rupanjan Rana, Risav Dwivedi, Ashutosh Dhar Chatterjee, Pushpita Mukkamala, Raghava Rao International Institute of Information Technology Department of Electronics and Communication Naya Raipur India Copenhagen Business School Centre for Business Data Analytics Department of Digitization Frederiksberg Denmark Tennessee State University Department of Computer Science Nashville United States

ISBN: (纸本)9781665459754

With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown. © 2022 IEEE.

关键词： 5G mobile communication systems

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A Cybersecurity Framework for Home Energy Management Systems using Artificial Intelligence

A Cybersecurity Framework for Home Energy Management Systems...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Lakshitha Gunasekara Harsha Moraliyage Daswin De Silva Nishan Mills Damminda Alahakoon Andrew Jennings Milos Manic Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

Home Energy Management Systems (HEMS) have become a pivotal component of the smart grid infrastructure as they facilitate energy exchange and trading. With the increasing use of micropower renewables generation from wind and solar within residential houses and buildings, HEMS now plays a critical role in not just managing energy usage but assisting the smart grid with schedule optimisation. However, HEMS are typically installed with limited security measures, which makes them vulnerable to cyber-attacks. A coordinated attack on multiple HEMS has the potential to compromise the entire power grid. In this article, we aim to address this gap by proposing a robust security framework for HEMS. The framework centralises security in the design of HEMS and leverages Artificial Intelligence for the security requirements. The proposed framework consists of five layers with built-in security measures that use AI to mitigate cyber threats. This is a key contribution that has been neglected in related work in optimizing HEMS operations.

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CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

arXiv

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arXiv 2023年

作者： Sun, Shoukun Xian, Min Xu, Fei Capriotti, Luca Yao, Tiankai Machine Intelligence and Data Analytics Lab Department of Computer Science University of Idaho United States Idaho National Laboratory United States

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing feedback from the output. However, in most state-of-theart approaches, 1) the inference stage involves inflexible heuristic rules and requires a separate refinement model, and 2) the number of user clicks and model performance cannot be balanced. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively. © 2023, CC BY.

关键词： Image segmentation

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Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

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computers, Materials & Continua 2021年第8期68卷 2363-2376页

作者： Muhammad Usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq Al-khawarizmi Institute of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan UniversityRiyadhSaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),*** treatment methods for ADHD are either psycho-stimulant medications or cognitive *** traditional methods,namely therapy,need a large number of visits to hospitals and include *** could be used for the effective treatment of *** could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain *** paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial *** proposed system consists of a puzzle game where its complexity increases with each *** signals were acquired using the Neurosky headset;then sent the signals to the designed gaming *** neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation *** results show that this game improves player overall performance from 74%to 98%by playing each game level.

关键词： Neurogaming brain-computer interface attention deficit hyperactivity disorder electroencephalography spatial memory cognitive skills attention level

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Human and AI Perceptual Differences in Image Classification Errors

arXiv

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arXiv 2023年

作者： Liu, Minghao Wei, Jiaheng Liu, Yang Davis, James Department of Computer Science and Engineering University of California Santa Cruz United States Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China

Artificial intelligence (AI) models for computer vision trained with supervised machine learning are assumed to solve classification tasks by imitating human behavior learned from training labels. Most efforts in recent vision research focus on measuring the model task performance using standardized benchmarks such as accuracy. However, limited work has sought to understand the perceptual difference between humans and machines. To fill this gap, this study first analyzes the statistical distributions of mistakes from the two sources and then explores how task difficulty level affects these distributions. We find that even when AI learns an excellent model from the training data, one that outperforms humans in overall accuracy, these AI models have significant and consistent differences from human perception. We demonstrate the importance of studying these differences with a simple human-AI teaming algorithm that outperforms humans alone, AI alone, or AI-AI teaming. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Attention-based Network for Image/Video Salient Object Detection

Attention-based Network for Image/Video Salient Object Detec...

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European Workshop on Visual Information Processing (EUVIP)

作者： Omar Elharrouss Soukaina ElIdrissi ElKaitouni Younes Akbari Somaya Al-Maadeed Ahmed Bouridane Department of Computer Science Qatar University Doha Qatar Department of Computer Science Sidi Mohamed Ben Abdellah Univerisity Fez Morocco Centre for Data Analytics and Cybersecurity University of Sharjah Sharjah UAE

The goal of video or image salient object detection is to identify the most important object in the scene, which can be helpful in many computer vision-based tasks. As the human vision framework has a successful capacity to effortlessly perceive locales of interest from complex scenes, salient object detection mimics a similar concept. However, the salient object detection (SOD) of complex video scenes is a challenging task. This paper mainly focuses on learning from channel and Spatiotemporal representations for image/video salient object detection. The proposed method consists of three levels, the frontend, the attention models, and the backend. While the frontend consists of VGG backbone which ultimately learns the representation of the common and the discrimination features. After that, both Attention, Channel-wise, and Spatiotemporal models are applied to highlight the significant object using a feature detector and to calculate the spatial attention. Then the output features are fused to obtain the final saliency result. Experimental investigation evaluations confirm that our proposed model has proved its validity and effectiveness compared with the state-of-the-art methods.

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Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Legapriyadharshini, N. Thirumalaikumari, T. Senbagam, K. Sarasu, R. Basha, H. Anwer Malathi, P. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350309140

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers. © 2024 IEEE.

关键词： Legacy systems

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A Harmful Disorder: Predictive and Comparative Analysis for fetal Anemia Disease by Using Different Machine Learning Approaches

A Harmful Disorder: Predictive and Comparative Analysis for ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Mahadi Hasan Mst. Sazia Tahosin Afia Farjana Md. Alif Sheakh Md Maruf Hasan MS in Data Analytics The University of Tennessee Chattanooga USA Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science University of South Dakota Vermillion SD USA

Anemia is a major issue for public health with significant implications for national development, it remains a largely neglected health problem in many developing countries. Iron deficiency is responsible for at least 50% of all cases of anemia and kills nearly 1 million people each year. Africa and Southeast Asia account for three quarters of these deaths. Surprisingly, one of the top ten risk factors that contributes to the global burden of disease is iron deficiency anemia (IDA). This study investigated the use of machine learning models to predict anemia. The study compared the performance of five different machine learning models: K-Nearest Neighbors, Logistic Regression, Support Vector Machines, Gaussian Naive Bayes, and Light Gradient Boosting Machines. These models are combined using a voting classifier method to improve prediction accuracy. The study highlights the importance of accurately predicting diseases in the medical field. The ability to predict anemia at the right time is essential for effective prevention and treatment. This study demonstrates the potential of machine learning models to predict anemia and improve disease prevention and treatment. Using advanced algorithms and data processing techniques can help doctors make accurate predictions and make decisions, leading to better patient outcomes. This research result shows that the voting classifier achieved 99.95% accuracy.

关键词： Support vector machines Machine learning algorithms Digital forensics Predictive models Developing countries Prediction algorithms Iron

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Approximately Optimal Search on a Higher-dimensional Sliding Puzzle

arXiv

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arXiv 2024年

作者： Merleau, Nono S.C. O’Malley, Miguel Roldán, Érika Mukherjee, Sayan Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Germany Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

Higher-dimensional sliding puzzles are constructed on the vertices of a d-dimensional hypercube, where 2d−l vertices are distinctly coloured. Rings with the same colours are initially set randomly on the vertices of the hypercube. The goal of the puzzle is to move each of the 2d − l rings to pre-defined target vertices on the cube. In this setting, the k-rule constraint represents a generalisation of edge collision for the movement of colours between vertices, allowing movement only when a hypercube face of dimension k containing a ring is completely free of other rings. Starting from an initial configuration, what is the minimum number of moves needed to make ring colours match the vertex colours? An algorithm that provides us with such a number is called God’s algorithm. When such an algorithm exists, it does not have a polynomial time complexity, at least in the case of the 15-puzzle corresponding to k = 1 in the cubical puzzle. This paper presents a comprehensive computational study of different scenarios of the higher-dimensional puzzle. A benchmark of three computational techniques, an exact algorithm (the A* search) and two approximately optimal search techniques (an evolutionary algorithm (EA) and reinforcement learning (RL)) is presented in this work. The experiments show that all three methods can successfully solve the puzzle of dimension three for different face dimensions and across various difficulty levels. When the dimension increases, the A* search fails, and RL and EA methods can still provide a generally acceptable solution, i.e. a distribution of a number of moves with a median value of less than 30. Overall, the EA method consistently requires less computational time, while failing in most cases to minimise the number of moves for the puzzle dimensions d = 4 and d = 5. © 2024, CC BY.

关键词： Evolutionary algorithms

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