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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

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computers, Materials & Continua 2022年第6期71卷 5639-5657页

作者： Faisal Saeed Mohammad Al-Sarem Muhannad Al-Mohaimeed Abdelhamid Emara Wadii Boulila Mohammed Alasli Fahad Ghabban College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUnited Kingdom Information System Department Saba’a Region UniversityMareebYemen Computers and Systems Engineering Department Al-Azhar UniversityCairo11884Egypt RIADI Laboratory National School of Computer SciencesUniversity of ManoubaManouba2010Tunisia

Several millions of people suffer from Parkinson’s disease ***’s affects about 1%of people over 60 and its symptoms increase with *** voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech *** the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this *** classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many *** paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and *** dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 *** experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

关键词： Filter-based feature selection methods machine learning parkinson’s disease wrapper-based feature selection methods

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system for coronavirus classification based on a new textural descriptor 21

System for coronavirus classification based on a new textura...

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Boubakri, Fathia Gargouri, Norhene Masmoudi, Alima Damak Sfax Tunisia Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group Tunisia Sfax Engineering School University of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering P.O. Box 1169 Sfax3038 Tunisia

ISBN: (纸本)9781665482615

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve (A-{z}) of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists. © 2022 IEEE.

关键词： COVID-19

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Genetic Algorithm Audio Watermarking using Multiple Image-based Watermarks

Genetic Algorithm Audio Watermarking using Multiple Image-ba...

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2007 International Symposium on Communications and Information Technologies (ISCIT 2007), vol.3

作者： Mahasak Ketcham Sartid Vongpraphip Digital System Engineering Laboratory Department of Computer Engineering Faculty of Engineering Chulalongkom University Bangkok Thailand Digital System Engineering Laboratory Department of Computer Engineering Faculty Engineering Chulalongkom University Bangkok Thailand

This article presents the audio watermarking using multiple image-based watermarks based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. Rather than using one watermark into a whole length of audio, we select appropriate watermarks in order to embed it into selected parts. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.

关键词： Watermarking Genetic algorithms Information technology Multimedia systems Internet Large-scale systems Copyright protection Cryptography Optimization methods Steganography

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A distributed wearable system based on multimodal fusion

A distributed wearable system based on multimodal fusion

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3rd International Conference on Embedded Software and systems, ICESS 2007

作者： Cho, Il-Yeon Sunwoo, John Jeong, Hyun-Tae Son, Yong-Ki Ahn, Hee-Joong Lee, Dong-Woo Han, Dong-Won Lee, Cheol-Hoon Digital Home Research Division Electronics and Telecommunications Research Institute Daejeon Korea Republic of System Software Laboratory Department of Computer Engineering Chungnam National University Daejeon Korea Republic of

ISBN: (纸本)3540726845

Wearable computer can be worn anytime with the support of unrestricted communications and variety of services which provides maximum capability of information use. Key challenges in developing such wearable computers are level of comfort that users do not feel what they wear, easy and intuitive user interface and power management technique. This paper suggests a wearable system that consists of a wristwatch-type gesture recognition device and a personal mobile gateway that functional input/output modules can be freely plugged in and taken out. We describe our techniques implemented during our wearable system development: 1) multimodal fusion engine that recognizes voice and gesture simultaneously, 2) power management technique and 3) gesture recognition engine. Finally, we evaluate the performance of our multimodal fusion engine, and show the power consumption measurement data of our system built with the power management technique. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Distributed computer systems

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection system(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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Leveraging Artificial Intelligence for Innovative Technopreneurial Business Models 3

Leveraging Artificial Intelligence for Innovative Technopren...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Sudaryono Yusuf, Inayatul Izzati Diana Maulana, Sabda Pratiangga, Aptanta Apriliasari, Dwi University of Raharja Department of Informatics Engineering Tangerang Indonesia Bina Bangsa University Department of Informatics Engineering Serang Indonesia Bosindo Department of Digital Business Tangerang Indonesia Rey Department of Computer Science Tangerang Indonesia Pandawan Sejahtera Indonesia Department of Information System Tangerang Indonesia

ISBN: (纸本)9798350367492

This study examines the rapid growth of startups integrating artificial intelligence (AI) into their business models, aiming to analyze and compare these startups with traditional IT organizations to understand their differences. Analyzing 162 global startups, this research identifies four unique archetypal business models, introducing novel methodologies in AI integration and data utilization that contrast with conventional IT paradigms. The focus is on technologies providing data analytics solutions, AI products and services, and the expansion of AI use. Key components of AI startup business models are highlighted: (1) advantages of AI in new value propositions, (2) data use to create value, and (3) the impact of AI on business decision-making. This research enhances the understanding of AI startup business models and suggests future research directions in entrepreneurship. The developed taxonomy highlights four distinct AI business models: the Deep Tech Researcher, Data Analytics Provider, AI Product/Service Provider, and AI Development Facilitator, each offering unique value propositions and leveraging AI technologies in different ways to address specific market needs. The research framework fosters entrepreneurial efforts with taxonomies and models as useful instruments. © 2024 IEEE.

关键词： Artificial Intelligence Data Analytics Startup Business Models Technopreneurship

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Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

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computer systems Science & engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)*** major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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An improved method to embed larger image in QR code

An improved method to embed larger image in QR code

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Chanon Skawattananon Sartid Vongpradhip Digital System Engineering Laboratory Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781479908059

Embedding the owner image is the fundamental concept in identifying the ownership of the QR code. However, the image larger than the percentage of error correction, embedded in the QR code, could not be read. This research try to increase the size of the embedding image by investigating the change of encoding the pad characters and the adjustment of intensity of light. The largest image size that can be embedded and read through general QR code reader, with different errors correction levels, is found. The results are compared with other research on embedding an image in except the finder patternof QR code and our embedding images size are larger in all the correction levels.

关键词： Error correction codes Encoding Image coding Error correction ISO standards IEC standards Decoding

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An Intelligence Audio Watermarking Based on DWT- SVD Using ATS

An Intelligence Audio Watermarking Based on DWT- SVD Using A...

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GCIS, WRI Global Congress on Intelligent systems

作者： Sartid Vongpraphip Mahasak Ketcham Digital System Engineering Laboratory Department of Computer Engineering Faculty of Engineering Chulalongkom University Bangkok Thailand

All three, discrete wavelet transform (DWT)-singular value decomposition (SVD) and adaptive tabu search (ATS) have been used as mathematical tools for embedding data into an audio signal. In this paper, we present a new robust audio watermarking scheme based on DWT-SVD and ATS. After applying the DWT to the cover audio signal, we map the DWT coefficients, and apply the SVD, we search for the optimal intensity of audio watermarking by using the ATS. Experimental results show that the watermarking method performs well in both security and robustness to many digital signals processing, such as filtering, cropping, mp3 and random noise.

关键词： Watermarking Discrete wavelet transforms Data engineering Singular value decomposition Matrix decomposition Intelligent systems Data security Multimedia systems Internet Copyright protection

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