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Optimizing IoT Threat Mitigation with Artificial Intelligence in Banking: A Multi-Objective Approach 2

Optimizing IoT Threat Mitigation with Artificial Intelligenc...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Pratap Singh Chauhan, Rudra Sonker, Sanjav Kumar Kaur, Manpreet Sharma, Chhaya Singh, Robin Singh, Ramendra Shri Shankracharya Institute of Professionals Management and Technology Department of CSE-AI and Ai Ml Raipur India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Institute of Computing Chandigarh University Gharuan Mohali India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering Ghaziabad India Graphic Era Hill University Dehradun India Ghaziabad India

ISBN: (纸本)9798350371055

This research introduces a novel ai-based mechanism for optimizing threat mitigation in IoT banking systems, addressing the growing vulnerabilities in this critical sector. The proposed mechanism, characterized by a precision of 0.88 and a balanced recall of 0.79, offers a robust defense against cyber threats. Leveraging a Deep Neural Architecture known as Pointer Networks, the mechanism adapts dynamically, ensuring high accuracy in threat identification (precision) while comprehensively covering potential threats (recall), resulting in a harmonious F1 score of 0.83. Through extensive threat-specific evaluations, the mechanism proves versatile, exhibiting high performance in scenarios involving malware (precision: 0.89, recall: 0.82, F1 score: 0.85), denial of service (DoS) attacks (precision: 0.87, recall: 0.78, F1 score: 0.82), and unauthorized access attempts (precision: 0.90, recall: 0.81, F1 score: 0.85). Scalability testing further validates its practical applicability, maintaining precision and F1 score values across varying sizes of IoT ecosystems. This research establishes the proposed mechanism as a potent and adaptable cybersecurity tool, poised to fortify the resilience of IoT banking systems against the dynamic landscape of cyber threats. © 2024 IEEE.

关键词： Artificial Intelligence Banking Cybersecurity IoT Threat Mitigation

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Genetic Signal Processing for Categorizing Genomic Data using Convolutional Neural Networks

Genetic Signal Processing for Categorizing Genomic Data usin...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Pulicherla, Padmaja Aluvala, Srinivas Premchander, Munimanda Sai Sudha, Poranki V L R N Revanasiddappa Institute of Technology and Management Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Breakthroughs in high-throughput sequencing technology have resulted in exponential growth in acquired genetic data. Classic machine learning systems have difficulties in dealing with complex genetic data, leading to investigation of deep learning methods. A unique technique for categorizing genomic data using convolutional neural networks (CNNs) is described. The genetic signal processing approach can extract unique characteristics from genomic data. CNNs' ability to comprehend subtle patterns and correlations in cleaned-up data results in improved classification accuracy. A publicly accessible dataset is used to compare the proposed technique across various criteria. The study of genetic information using digital noise processing technology is known as genetic signal processing (GSP). GSP are used to turn the nucleotide sequence into a line graph, allowing for appropriate pattern classification. From the results, it clearly shows that proposed CNN achieves better results in terms of accuracy (0.96), precision (0.96), sensitivity (0.95), specificity (0.97) and f1-score (0.95). © 2023 IEEE.

关键词： Convolutional layers Convolutional neural networks genetic signal processing Genetic signals Genomic data

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Reconfigurable Intelligent Surface-Assisted Beam Management for ai Signal Classification in mmWave Networks

Reconfigurable Intelligent Surface-Assisted Beam Management ...

引用

2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Shtaiwi, Eyad Sklivanitis, George Pados, Dimitris A. The Center for Connected Autonomy and AI The Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

ISBN: (纸本)9781946815200

Millimeter wave (mmWave) communications are vulnerable to blockages and node mobility due to the highly directional signal beams. Reconfigurable intelligent surface (RIS) has emerged as a competitive solution to the blocking problem. However, due to the passive nature of the RIS, obtaining channel state information (CSI) for RIS-assisted mmWave communication systems is rather difficult. Automatic modulation classification (AMC) could improve the transmission efficiency of mmWave wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment, under the condition of unknown prior information, such as CSI, thus improving spectral efficiency and reducing the power consumption in NextG wireless communications systems. Deep learning (DL) has been widely used for AMC in sub-6 GHz wireless networks due to its capacity for feature learning directly on raw radio front-end data. © 2025 USNC-URSI.

关键词： Image coding

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Hierarchical Pronunciation Assessment with Multi-Aspect Attention 48

Hierarchical Pronunciation Assessment with Multi-Aspect Atte...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Do, Heejin Kim, Yunsu Lee, Gary Geunbae Postech Graduate School of Ai Korea Republic of Postech Department of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9781728163277

Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels of granularity such as phoneme, word, and utterance, with diverse aspects such as accuracy, fluency, and completeness, is essential. However, existing multi-aspect multi-granularity methods simultaneously predict all aspects at all granularity levels;therefore, they have difficulty in capturing the linguistic hierarchy of phoneme, word, and utterance. This limitation further leads to neglecting intimate cross-aspect relations at the same linguistic unit. In this paper, we propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model, which hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention that reflects associations across aspects at the same level to create more connotative representations. By obtaining relational information from both the granularity- and aspect-side, HiPAMA can take full advantage of multi-task learning. Remarkable improvements in the experimental results on the speachocean762 datasets demonstrate the robustness of HiPAMA, particularly in the difficult-to-assess aspects. © 2023 IEEE.

关键词： Pronunciation assessment

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YOLOv8- Based Helmet and Vest Detection System for Safety Assessment

YOLOv8- Based Helmet and Vest Detection System for Safety As...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Divyanka Thakur Priya Pal Amogh Jadhav Numaira Kable Bhagyalakshmi V Sonali Deshpande Department of Computer Science Engineering (AI&ML) Smt. Indira Gandhi College of Engineering Navi Mumbai India Department of Electronics and Telecommunication KC College of Engineering Management and Research Thane(E) India

Helmet and vest detection systems ensure worker safety in high-risk professions like construction, mining, and law enforcement. The latest version of the You Only Look Once (YOLO) object detection model, YOLOv8, offers significant advancements over its predecessors. YOLOv8 utilizes a fully convolutional neural network, comprising two major components: the backbone and the head, incorporating a self-attention mechanism to identify objects at various scales in the image. This model eliminates anchors, reducing box predictions and speeding up post-processing phases. Among the YOLOv8 family, YOLOv8-s stands out as the smallest and fastest model, with a size of 21 MB and a single epoch runtime of only 11 seconds. In this study, the YOLOv8-s model achieved an impressive 99.5% mean average precision (mAP) accuracy rate in real time. By implementing this system, the construction industry can significantly enhance safety practices through efficient and reliable monitoring of personal protective equipment usage at construction sites.

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Advancing Early Detection: CNN for Automated Blood Cancer Diagnosis and Anomaly Detection 2

Advancing Early Detection: CNN for Automated Blood Cancer Di...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raji, V. Seetharaman, Anburaman Kasi, Baskar Govindaraju, Sakthi Paarkavy, B. S K P Engineering College Department of Computer Science and Engineering Tiruvannamalai India Sri Venkateswaraa College of Technology Department of Artificial Intelligence and Data Science Chennai India Galgotias University School of Computer Science and Engineering Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India Saveetha Engineering College Department of AI&DS Chennai India

ISBN: (纸本)9798350368109

The evaluation and diagnosis of diseases related to blood cancer can be intricate and time-consuming. This complexity is compounded by the reliance on manual analyses employing techniques that consume a significant amount of time. Numerous methods have been created over the past ten years for the finding, analysis, and categorization of human blood cancer. The lack of methods or models that can automatically analyze human blood cells to detect the presence of cancer, however, is still a significant issue. The development of such a model holds the potential to enhance the early detection and prevention of these diseases, thereby facilitating more prompt medical diagnoses. In our ongoing research, we present our current endeavors in the creation of a Deeper with Convolutions Neural Network (DCNN) Learning Model capable of recognizing anomalies in blood cells. We employ a Hybrid Ensemble DCNN Learning technique for the identification of blood cancer. With accuracy rates surpassing 99%, this method is one of the most sophisticated deep learning models. © 2024 IEEE.

关键词： Contrastive Learning

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High-Capacity, A Secret Video Sharing Using DM-Stego and Genetic Algorithm

High-Capacity, A Secret Video Sharing Using DM-Stego and Gen...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Logeshwari, R. Velswamy, Rajasekar Subhashini, R. Karunakaran, V. Srm Institute of Technology Department of Network and Communication Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Dept. of CSE-AI and Ml Coimbatore India

ISBN: (纸本)9798350300857

Electronic media knowledge is unprecedently increasing in recent years. In almost all security control areas, traffic control, weather monitoring, video conferences, social media etc., videos and multimedia data analysis practices are used. As a consequence, it is necessary to retain and transmit these data, by considering the security and privacy issues. IN this research study, a new Div-Mod Stego algorithm is combined with the Multi-Secret Sharing method along with temporary frame reordering and Genetic algorithm to implement high-end security in the process of video sharing. The qualitative and quantitative analysis has also been carried out to compare the performance of the proposed model with the other existing models. A computer analysis shows that the proposed solution would satisfy the requirements of the real-time application. © 2023 IEEE.

关键词： Genetic algorithms

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Multi-Stage Predict+Optimize for (Mixed Integer) Linear Programs 38

Multi-Stage Predict+Optimize for (Mixed Integer) Linear Prog...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Stuckey, Peter J. Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science University of California Davis United States Department of Data Science and AI Monash University Clayton Australia

The recently-proposed framework of Predict+Optimize tackles optimization problems with parameters that are unknown at solving time, in a supervised learning setting. Prior frameworks consider only the scenario where all unknown parameters are (eventually) revealed at the same time. In this work, we propose Multi-Stage Predict+Optimize, a novel extension catering to applications where unknown parameters are instead revealed in sequential stages, with optimization decisions made in between. We further develop three training algorithms for neural networks (NNs) for our framework as proof of concept, all of which can handle mixed integer linear programs. The first baseline algorithm is a natural extension of prior work, training a single NN which makes a single prediction of unknown parameters. The second and third algorithms instead leverage the possibility of updating parameter predictions between stages, and trains one NN per stage. To handle the interdependency between the NNs, we adopt a sequential and parallelized versions of coordinate descent for training. Experimentation on three benchmarks demonstrates the superior learning performance of our methods over classical approaches. © 2024 Neural information processing systems foundation. All rights reserved.

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Role of RPA to Seize Opportunities in 5G Edge Computing in Telecommunications

Role of RPA to Seize Opportunities in 5G Edge Computing in T...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Tatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science Williamsburg United States University of the Central Missouri Department of Computer Information Systems Warrensburg United States University of the Cumberlands Department of Information Technology Williamsburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderabad India Bangalore Institute of Technology Department of Ai and Ml Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

This paper explores the role of Robotic Process Automation (RPA) in leveraging opportunities presented by 5G edge computing in the telecommunications industry. Integrating RPA and 5G edge computing offers numerous benefits, including enhanced operational efficiency, real-time decision-making, improved service delivery, and cost savings. However, it also poses challenges such as complex integration, security concerns, and the need for effective change management. The paper comprehensively analyzes the potential benefits, challenges, and recommendations for implementing RPA in 5G edge computing. It explores use cases, future advancements, emerging technologies, and anticipated opportunities and challenges. The findings highlight the need for clear objectives, process optimization, collaboration between IT and business teams, and continuous monitoring to leverage RPA in 5G edge computing effectively. © 2024 IEEE.

关键词： Telecommunication industry

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Real-Time Wrist Fracture Detection Using YOLO11

Real-Time Wrist Fracture Detection Using YOLO11

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Nellore Sai Nikhil Vemula Kesavaaditya Gupta Sai Harshavardhan Srungavarapu Gouranga Mandal School of Computer Science and Engineering VIT - AP University Amaravati Andhra Pradesh India Department of Computer Science and Engineering (AI & ML) Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

More than 1.71 billion individuals globally are impacted by bone diseases, especially conditions related to the musculoskeletal system, with fractures being a major worry. Prompt diagnosis is crucial, however, overlooked fractures are frequent in emergency situations, resulting in care delays. ai, particularly deep learning (DL), is becoming increasingly popular for aiding radiologists in identifying fractures in medical images. This review examines how deep learning is used in bone imaging. We employed the YOLO11 model, an algorithm for detecting objects in real-time that can forecast both bounding boxes and class probabilities at once, to detect bone fractures in real-time. We analyzed a variety of bone fracture images from different sources. Sophisticated training methods were used to improve the model's precision and resilience. The YOLO11 model showed great efficiency in detecting and categorizing fractures, providing substantial promise for live clinical use and enhancing diagnostic speed in medical settings.

关键词： Deep learning Wrist YOLO Training Reviews Computational modeling Bones Real-time systems Medical diagnostic imaging Resilience

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