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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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Software Code De-Compilation Techniques and Approaches: A Comparative Study

Software Code De-Compilation Techniques and Approaches: A Co...

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Information technology (ACIT)

作者： Hamed Fawareh Yazan Al-Smadi Mohammad Hassan Faid AlNoor Fawareh Ali Elrashidi Dept. of Software Engineering Zarqa University Jordan Dept. of Computer Science Zarqa University Jordan Dept. Of Mathmitical Science Zarqa University Jordan Dept. of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

De-compilation is the process of executable files as input where the de-compilation process tries to get away the source code, or the majority of it, if the file's source code is lost or damaged for whatever cause. De-compilation has many uses such as code recovery, security testing, and errors corrections, however, de-compilation is cautious since it depends on certain approaches and techniques. Nowadays de-compilation done by many automated and semi-automated tools, but the accuracy and the efficiency of it depend on the proposed approaches and techniques to stand in front of reengineering challenges. This paper aims to highlight up-to-date prior contributions in the field where 8 published research papers from May 2020 until May 2023 were collected to investigate the state-of-art. As a result, some of the researchers tend to develop their own tools and others tend to use popular automated with different approaches and techniques where the majority of the researchers focused on C, C++, and JAVA programming languages where 50% were de-compilation on C language.

关键词： Hands Java Source coding Programming Software Libraries Generators Security Information technology Testing

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SigDLA: A Deep Learning Accelerator Extension for Signal Processing

arXiv

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arXiv 2024年

作者： Fu, Fangfa Zhang, Wenyu Jiang, Zesong Zhu, Zhiyu Li, Guoyu Yang, Bing Liu, Cheng Xiao, Liyi Wang, Jinxiang Li, Huawei Li, Xiaowei Department of Microelectronics Science and Technology Harbin Institute of Technology Harbin China SKLP Institute of Computing Technology Chinese Academy of Sciences Beijing China Dept. of Computer Science University of Chinese Academy of Sciences Beijing China Institute of Advanced Technology University of Science and Technology of China Hefei China Dept. of Computer Science and Technology Harbin University of Science of Technology Harbin China

Deep learning and signal processing are closely correlated in many IoT scenarios such as anomaly detection to empower intelligence of things. Many IoT processors utilize digital signal processors (DSPs) for signal processing and build deep learning frameworks on this basis. While deep learning is usually much more computing-intensive than signal processing, the computing efficiency of deep learning on DSPs is limited due to the lack of native hardware support. In this case, we present a contrary strategy and propose to enable signal processing on top of a classical deep learning accelerator (DLA). With the observation that irregular data patterns such as butterfly operations in FFT are the major barrier that hinders the deployment of signal processing on DLAs, we propose a programmable data shuffling fabric and have it inserted between the input buffer and computing array of DLAs such that the irregular data is reorganized and the processing is converted to be regular. With the online data shuffling, the proposed architecture, SigDLA, can adapt to various signal processing tasks without affecting the deep learning processing. Moreover, we build a reconfigurable computing array to suit the various data width requirements of both signal processing and deep learning. According to our experiments, SigDLA achieves an average performance speedup of 4.4×, 1.4×, and 1.52×, and average energy reduction of 4.82×, 3.27×, and 2.15× compared to an embedded ARM processor with customized DSP instructions, a DSP processor, and an independent DSP-DLA architecture respectively with 17% more chip area over the original DLAs. © 2024, CC BY-NC-SA.

关键词： Digital signal processing

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Customer Segmentation Using Hierarchical Clustering

Customer Segmentation Using Hierarchical Clustering

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International Conference for Convergence of technology (I2CT)

作者： Areeba Afzal Laiba Khan Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Aqsa Khalid Rimsha Awan Farhan Ashraf Zohaib Ahmed Khan Arslan Javaid Department of Computer Engineering Information Technology University Lahore Punjab Pakistan Dept. of Computer Science Bahria University Lahore Campus Department of Computer Science Faculty of Information Technology University of Central Punjab Lahore Pakistan Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Information Technology University Lahore Pakistan Department of Computer Science National College of Business Administration & Economics Lahore Pakistan

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hierarchical clustering techniques for mall customer segmentation, with a focus on the paper titled ’MALL CUSTOMER SEGMENTATION USING MACHINE LEARNING’ as the benchmark. Our dataset encompasses a diverse range of mall customers, spanning demographics and behavioral attributes. Hierarchical clustering systematically groups customers into clusters, revealing distinct segments within the mall’s customer base. A comprehensive analysis of these clusters unveils profound insights into customer tendencies, preferences, and purchasing habits. These insights form a solid foundation for tailored marketing campaigns, personalized recommendations, and resource allocation within the mall. The study contributes significantly to customer analytics, providing retailers with a powerful tool to gain a competitive edge in the retail sector. By leveraging hierarchical clustering for mall customer segmentation, businesses can enhance customer satisfaction, drive sales, and foster lasting customer relationships. This paper underscores the importance of data-driven methodologies in understanding customer behavior and offers a practical framework for businesses to harness the potential of hierarchical clustering for strategic decision-making in the retail industry.

关键词： Industries Decision making Customer satisfaction Benchmark testing Solids Resource management Data mining

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Spintronic Device Potential in Neuromorphic Computing from a Device-Circuit Modeling Perspective

Spintronic Device Potential in Neuromorphic Computing from a...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Suman, Bashetty Deepika, N.M. Rajalakshm, B. Singh, Jay Nijhawan, Ginni Riyadh, Mazin Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Gl Bajaj Institute of Technology & Management Dept. of Eee U.P. Greater Noida India Lovely Professional University Phagwara India AL-Hameed College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

Recent years have seen tremendous progress in the area of neuromorphic computing, which seeks to mimic the parallel processing power and energy efficiency of the human brain. From the standpoint of device-circuit modelling, this work explores how spintronic devices might improve the scalability and performance of neuromorphic systems. Utilizing the inherent spin of the electron and the magnetic moment that goes along with it, spintronics has special benefits in terms of non-volatility, low power consumption, and high integration density. We provide a thorough model that accurately depicts the complex behaviour of spintronic components and how they are incorporated into neuromorphic circuits. Our results suggest that spintronic devices may improve the overall efficiency of neuromorphic systems by reducing the power-delay product substantially. Furthermore, it is possible to realize synaptic capabilities and plasticity processes with better accuracy by using the intrinsic features of spintronic devices, such as magnetoresistance and spin-transfer torque. Additionally, the research identifies problems with device variability and suggests possible fixes. In summary, spintronic devices have great potential to transform the field of neuromorphic computing if they are properly modelled and integrated. © 2023 IEEE.

关键词： Device-Circuit Modeling Magnetoresistance. Top of Form Neuromorphic Computing Spintronics Synaptic Plasticity

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Federated Digital Twin

Federated Digital Twin

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Christian Vergara Rami Bahsoon Georgios Theodoropoulos Wendy Yanez Nikos Tziritas School of Computer Science University of Birmingham Birmingham UK Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos School of Computer Science University of Birmingham Birmingham UK Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system.

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Data Extraction from an Electrocardiogram Signal and QRS Peak Detection Employing Wavelet-Based Approach 3

Data Extraction from an Electrocardiogram Signal and QRS Pea...

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3rd International Conference for Emerging technology, INCET 2022

作者： Ahmed Himu, Sheik Erfan Kalam, Safayet Das, Rana Nayem, Jannatul Sirajudeen, Safdhan Kabir, Md. Maharaj Abrar, Fahim Choity, Tasnia Fahrin International Islamic University Chittagong Dept. of Electrical and Electronic Engineering Chittagong Bangladesh South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9781665494991

The electrocardiogram (ECG) is a powerful diagnostic tool for attaining physiological signals. It can detect a wide range of information about a patient's pathophysiological individual state. A wavelet transform (WT) based method is introduced in this paper to analyze ECG signals. Reviewing the theory behind ECG and WT as well as identifying and summarizing the potential applications for applying WT to examine ECG are also done in this paper. Moreover, an algorithm is applied on the basis of WT to perform ECG beat segmentation and subsequently extract useful clinical parameters. ECG data from the physio-net as well as real patient data will be tested with this algorithm. The results demonstrate that even subtle changes in the ECG from synthetic data can be detected through the algorithm. Afterwards, the performed test data will be compared with previous work. So, this newly introduced system has become reliable and significant for biomedical research. © 2022 IEEE.

关键词： Electrocardiograms

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Channel Allocation in 5G Networks using Ant Colony Optimization Algorithm

Channel Allocation in 5G Networks using Ant Colony Optimizat...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Areej Hussein Mohammad Anbar Mohammed Molhem Atheer Yousif Oudeh Mohammad Nassr Habib Fardoun Dept. of Communication Technology Engineering University of Tartous Tartous Syria Dept. of Computer Engineering Technology Al-Ayen University Thi-Qar Iraq Dept. of Computer Science College of Education for pure Science University of Thi-Qar Scientific Research Center Al-Ayen University Thi-Qar Iraq Canadian University Dubai UAE

Efficient channel allocation is crucial for enabling concurrent calls and data sessions within a cellular network in general. When channels are inefficiently allocated, network congestion may occur leading to call blocking due to a lack of available channels. In this research, an Ant Colony Optimization (ACO) algorithm-based model has been proposed to tackle channel allocation problem in 5G cellular networks. The proposed model has been simulated using Python. The experimental study focused on minimizing Call Block Probability (CBP) by optimizing channel allocation as the number of network users increase.

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