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Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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Physical Analysis of Lateral-BTBT Induced GIDL Current in GaN-Based FinFET Devices 11

Physical Analysis of Lateral-BTBT Induced GIDL Current in Ga...

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11th IEEE Open Conference of Electrical, Electronic and information Sciences, eStream 2024

作者： Rajawat, Vandana Singh Kumar, Ajay Choudhary, Bharat Malaviya National Institute of Technology Department of Electronics and Communication Engineering Jaipur India Jaypee Institute of Information Technology Department of Electronics and Communication Engineering Noida India

ISBN: (纸本)9798350352412

In this paper, we have analyzed the behavior of the Lateral-BTBT component of gate-induced drain leakage (GIDL) current on various configurations of GaN FinFET with High-k oxide which are Bulk-GaN FinFET, JAM-GaN FinFET and JL-GaN FinFET. It also studied how, in the OFF state (VGS=0), Lateral-BTBT causes parasitic BJT to form for different configurations. It was also observed that parasitic BJT phenomena are significant only in JAM-GaN FinFET and Bulk- GaN FinFET. JL-GaN FinFET offers minimum leakage current and maximum Ion/Ioff ratio which is ∼6.5 × 109. In addition, the effect of variation in source and drain doping on Bulk-GaN FinFET and JAM-GaN FinFET and their energy band profile are also studied. © 2024 IEEE.

关键词： FinFET

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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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Blockchain-based alcoholic beverages supply chain management system

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2493-2523页

作者： Chen, Chin-Ling Lim, Zi-Yi Liao, Hsien-Chou School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology 168 Jifeng East Road Taichung413310 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology 168 Jifeng East Road Taichung413310 Taiwan

Nearly 3 million people die each year as a result of dangerous alcohol consumption, according to the World Health Organization, factoring for 5.3% of all deaths globally. Many international organizations and governments have begun to regulate the people who would use alcoholic beverages. More countries have agreed to increase their tax policies to increase their national income by lowering consumer willingness to buy. As the cost of alcoholic beverages rises, more illicit producers produce or smuggle illegal alcoholic beverages. There’re able to prevent illicit alcoholic beverages from accessing the market by controlling the supply chain and conducting regular inspections of illegal alcoholic beverages. We need to deploy a traceable supply chain system to discover the origin of a product during its manufacturing and distribution in order to control the supply chain of alcoholic beverages. With the conventional supply chain database system, the company or malicious individuals may arbitrarily tamper with the information for their benefit. As a result, this research proposed an overall design for an alcoholic beverage supply chain management system based on blockchain technology (Hyperledger Fabric) to avoid data tampering and ensure data integrity. information security technology is also applied to create a secure system that ensures data and communication security. Unforgeable data, traceable data, non-repudiation, high data integrity, and openness to all players are all features of the system. The blockchain network’s throughput and latency are examined to ensure that the performance is adequate for use and deployment in a commercial application. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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Real-Time Power Estimation and Monitoring of an IoT Node Using MQTT 6th

Real-Time Power Estimation and Monitoring of an IoT Node Usi...

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6th International Conference on Intelligent communication, Control and Devices, ICICCD 2024

作者： Sharma, Ritesh Kumar Verma, Gaurav Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida Noida India

ISBN: (纸本)9789819783281

Measurement of energy consumption is an important part of the embedded and IoT system designing. Most of these systems are battery operated or have limited power resources, so to design power efficient systems it is imperative to have some mechanism for power or energy estimation. The paper presents a cloud-based power profiling system for embedded devices. The system determines the current by measuring voltage drop across a shunt resistor which is connected in series with the test node. The power measurement data is uploaded on a cloud server using MQTT protocol. This method provides real-time monitoring of the power consumption of the devices under test. A dashboard is built on the cloud platform for real-time monitoring of power-related information about the test device. The data can also be stored on a local system as well using UART communication. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Enhancing Ddos Attack Detection and Prevention in Mobile IP Networks Using HCIF 5

Enhancing Ddos Attack Detection and Prevention in Mobile IP ...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Shirke, Sonali Bharat Jaleel, Uruj Kadam, Sandeep Junnarkar, Aparna Atul Kalinga University Department of Computer Sci. and Engg India Keystone School of Engineering Department of Computer Engineering India Vishwakarma Institute of Information Technology Department of Information Technology India

ISBN: (纸本)9798350376685

This research suggests a Hybrid Cache-based Intelligent Framework (HCIF) to help mobile IP networks better identify and mitigate DDoS attacks. To improve network security and QoS, the HCIF technique integrates source monitoring, attack detection, and verification based on Turing tests. We evaluate HCIF and the state-of-the-art RBPBoost approach in NS-3 simulations, taking into account different mobility situations and attacker numbers. Throughput, packet delivery ratio, and packet loss ratio are all areas where HCIF excels compared to RBPBoost, while delay performance is still an issue. © 2024 IEEE.

关键词： Internet protocols

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An empirical study to test the significant effect of bellwethers on predictive modeling in smart homes

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Journal of Data, information and Management 2024年第2期6卷 111-123页

作者： Mensah, Solomon Kudjo, Patrick Kwaku Brown, Selasie Aformaley Dick, Elizabeth Akuafum Koffie-Ocloo, Dzordzoe Woelorm Department of Computer Science University of Ghana Legon Accra Ghana Department of Information Technology Wisconsin International University College Accra Ghana Department of Information Technology University of Professional Studies Accra Ghana Department of Information Technology Ghana Communication Technology University Accra Ghana

Smart homes are envisioned and expected to be furnished with effective and reliable sensors for constant monitoring, sensing, and actuating the environment. Data from these sensors can be analyzed and used for the provision of various services by automating common tasks undertaken in the homes with minimal disruption to human activities. Thus, in order to provide these services, there is the need to obtain an intelligent system to accurately predict the most likely event to occur in a smart home. These intelligent systems rely on prediction algorithms trained from existing data for their functioning. The study seeks to invesigate the feasibility and significance of using Bellwethers for predictive modelling in smart homes. Recent works in the domain of software engineering – software defect and effort estimation have shown that the use of exemplary data (referred to as Bellwether) in the training set relatively improves prediction accuracy. These results were obtained based on an empirical analysis of the Bellwether effect i.e., the existence of Bellwethers in a given dataset. Existing studies in the context of smart homes focus on using all data from various sensors for training and validating the prediction algorithms for intelligent systems in smart homes. This mode of training and validating the prediction algorithms does not always yield accurate prediction results due to the variations and heterogeneous nature of the population dataset. A novel framework based on Markov chains and Deep learning algorithms is constructed to sample Bellwethers for predictive modelling in smart homes. It was observed that the sampled Bellwethers yielded improved prediction accuracy for the studied dataset as compared to using all data instances or population set (growing portfolio). It was evident that Bellwethers can improve the reliability and accuracy of intelligent systems built for smart homes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bellwether Intelligent Systems Internet of Things Prediction Smart Homes

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FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

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Journal of Cyber Security and Mobility 2023年第4期12卷 465-496页

作者： Shetty, Nisha P. Muniyal, Balachandra Priyanshu, Aman Das, Vedant Rishi Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem;however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy. © 2023 River Publishers.

关键词： Learning systems

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Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Distance Local Ternary Pattern EAdLTP for Image Classification 2nd

Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Di...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Misty, Most Marria Akter Abir, Md. Anwarul Islam Mondal, Sajal Islam, Md. Zahidul Islam, Md. Monirul Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Sheikh Hasina University Netrokona Bangladesh Department of Information and Communication Technology Islamic University Kushtia Bangladesh

ISBN: (纸本)9789819989362

This study introduces a new local feature descriptor called evaluation window-based adjacent distance local ternary pattern EAdLTP for image classification. It is created by combining evaluation window EwLBP and adjacent distance local ternary pattern (AdLTP) to achieve robustness by encoding adjacent information. EwLBP produces an evaluation window to reduce noise in the neighbor’s values, and AdLTP captures the relationships between sequential neighbors. The adjacent sub-image window and the adjacent neighbor window are used to calculate the neighbors and extract the binary code that is modified to improve the information of the adjacent neighbors. The final EAdLTP pattern is divided into two parts (EAdLTPU and EAdLTPL), and the feature descriptor vector is obtained by concatenating their histograms. The proposed EAdLTP descriptor is tested on the KTH-TIPS and KTH-TIPS2b datasets and consistently outperforms other fundamental methods by being more robust against noise. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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