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5th International Conference on applied Machine Learning, ICAML 2023

作者： Xu, Taotao Shanat, Musdi Bin Hj. Kah Le, Brendan Chan Zhang, Wei University Malaysia Faculty of Applied and Creative Arts Sarawak Sarawak94300 Malaysia University Malaysia Faculty of Computer Science and Information Technology Sarawak Sarawak94300 Malaysia

ISBN: (纸本)9798350341416

The application of Luoshan shadow puppet elements in creative goods can break through the single communication path of shadow play and make the communication process of Luoshan shadow play art more vivid and 3D. In this paper, based on the design of modeling elements of mountain shadow puppets, the characteristics of visual elements of shadow puppets creative goods are analyzed, and an intelligent design algorithm of shadow puppets creative goods based on deep learning (DL) is innovatively proposed. Based on the design of modeling elements of Luoshan shadow puppets, this paper analyzes the characteristics of visual elements of shadow puppet creative goods, puts forward an intelligent design algorithm of shadow puppet creative goods based on DL, and explores the computer-aided shadow puppets modeling design strategy driven by artificial intelligence. The results show that the F1 value of the algorithm is about 94%, and the efficiency of the algorithm is high. The validity and reliability of the method in this paper are effectively verified by the test, which provides a reliable and efficient method for the design of modeling elements of Luoshan shadow puppet creative goods. © 2023 IEEE.

关键词： Design

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Malaria Cell Classification Using CNN: A Deep Learning Approach

Malaria Cell Classification Using CNN: A Deep Learning Appro...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Ali, Muhammad Qasim Kouatly, Rand Karunanithi, Meerah Hassan, Ahmed Rizvi, Syed Arslan Abbas Puthiyapurayil, Nafeesath Parappurath Gik Institute of Engg. Sciences & Tech Faculty of Computer Science & Engg Topi Pakistan University of Europe for Applied Sciences Department of Business Potsdam Germany Berlin School of Business and Innovation Department of Computer Science Berlin Germany Qilu Institute of Technology School of Int. Manu. & Cont. Engg Jinan City China

ISBN: (纸本)9798350353907

Plasmodium malaria is a parasitic protozoan that causes malaria in humans. computer-aided detection of Plasmodium is a research area attracting significant interest. In the current paper, the results of different machine learning, neural network, and deep learning approaches are speculated to evaluate their performance on cell images from digital microscopy concerning the identification of Plasmodium. A publicly available dataset containing 27,558 individual images is utilized, with half of these images containing Plasmodium and the remaining consisting of uninfected cells. The dataset is randomly split into 80-20% groups for training and testing. Color normalization is performed, and all images are spatially reshaped to a predefined size that matches the chosen architecture configurations. A fast CNN model is developed to classify cells in specific cell images. Additionally, classification and ranking of algorithms for transfer learning are conducted using existing networks such as AlexNet, ResNet, VGG-16, and DenseNet. Alongside the evaluation of the bag-of-features model performance, a Support Vector Machine is also employed for classification. A generalized probability trait of an image is assigned only after thoroughly evaluating the probability values given by each CNN model developed for this purpose in this paper. The algorithm achieves an overall accuracy of 96.7% on the test dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning Model to Analyse Noisy Data by Scanning Probe Microscope 2

Machine Learning Model to Analyse Noisy Data by Scanning Pro...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Ramesh, T.R. Sreevani, N. Revathi, V. John Samuel Babu, G. Singh, Navdeep Kareem, Ali School of Sciences and Computer Studies Bengaluru India Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

The emergence of massive multidimensional datasets may be attributed to the fast advancement of spectral imaging technologies in scanning probes, electrons, and visual microscopy during the past decade. When reducing hyperspectral data to lower-dimensional materials-specific properties, functional fitting is often used. This method requires determining the parameters of the fit function, but it is recognized how to estimate it. The problem is that iterative approaches, such as least-square gradient descent, that attempt to fit noisy data functionally might provide misleading results because they are so vulnerable to first assumptions. They provide a method for reducing hyperspectral data by use of a machine learning technique. By combining machine learning with a least-squares method, the efficacy signal-to-noise ratio of band-excitation piezoresponse force microscopy is shown to be over an order of magnitude higher. This enables characterisation even with extremely tiny driving signals or weak material responses. © 2024 IEEE.

关键词： Probes

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Blockchain based Secure Data Storage Verification Algorithm for Smart City Environment 4

Blockchain based Secure Data Storage Verification Algorithm ...

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4th International Conference on Innovative Trends in Information technology, ICITIIT 2023

作者： Vivekanandan, Manojkumar Premkamal, Praveen Kumar Johnpaul, C.I. Ebinazer, Silambarasan Elkana School of Engineering and Applied Sciences SRM University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati522502 India Kamaraj College of Engineering and Technology Department of Computer Science and Engineering Virudhunagar India St. Thomas College Thrissur India Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (纸本)9781665494144

Blockchain based distributed ledger mechanism has got a wide range of applications in this era. The degree of security measurement is always a bottleneck. Since there are technologies to break it. Data sharing through cloud for smart cities, collaborative actions, remote activities based on the data at the source, etc., need to be secure and free from masquerading and tampering. In most of the cases the data is pushed into the cloud from access points, sensors, or remote access centers. Preventing the data access and identifying anonymous access to these sensors require an enhanced security mechanism that prevents the inconsistent data to be transferred to the cloud. We propose a blockchain based enhanced security system that protects the data from the access point it leaves for the cloud using a distributed ledger. The consensus mechanism ensures the trust of existing sources during the data transfer from the source to the cloud. The trust generated by the subsequent data blocks with the security hash key ensure the integrity of the data and validity of the actual source. This prevent the illegal access to the data sharing points. We have verified the degree of security offered by our proposed model using informal analysis. We found that our method has improved the security of data access. © 2023 IEEE.

关键词： Blockchain

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Contextual Decision-Making with Knapsacks Beyond the Worst Case 38

Contextual Decision-Making with Knapsacks Beyond the Worst C...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhaohua Ai, Rui Yang, Mingwei Pan, Yuqi Wang, Chang Deng, Xiaotie School of Computer Science Peking University Haidian Beijing China IDSS LIDS Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Management Science and Engineering Stanford University StanfordCA94305 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Dept. of Computer Science Northwestern University EvanstonIL60208 United States School of Computer Science Institute for Artificial Intelligence Peking University Haidian Beijing China

We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumptions that are further associated with an unknown random external factor. While previous research has already established an Oe(√T) worst-case regret for this problem, this work offers two results that go beyond the worst-case perspective: one for the worst-case gap between benchmarks and another for logarithmic regret rates. We first show that an Ω(√T) distance between the commonly used fluid benchmark and the online optimum is unavoidable when the former has a degenerate optimal solution. On the algorithmic side, we merge the re-solving heuristic with distribution estimation skills and propose an algorithm that achieves an Oe(1) regret as long as the fluid LP has a unique and non-degenerate solution. Furthermore, we prove that our algorithm maintains a near-optimal Oe(√T) regret even in the worst cases and extend these results to the setting where the request and external factor are continuous. Regarding information structure, our regret results are obtained under two feedback models, respectively, where the algorithm accesses the external factor at the end of each round and at the end of a round only when a non-null action is executed. © 2024 Neural information processing systems foundation. All rights reserved.

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The effect of deep learning methods on deepfake audio detection for digital investigation

The effect of deep learning methods on deepfake audio detect...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： McUba, Mvelo Singh, Avinash Ikuesan, Richard Adeyemi Venter, Hein Department of Computer Science University of Pretoria Pretoria South Africa Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and produce perceptually unrecognizable speech. These systems pose new protection and privacy risks to voice-driven interfaces. Fake audio has been used for malicious purposes and is difficult to classify what is real and fake during a digital forensic investigation. This paper reviews the issue of deep-fake audio classification and evaluates the current methods of deep-fake audio detection for forensic investigation. Audio file features were extracted and visually presented using MFCC, Mel-spectrum, Chromagram, and spectrogram representations to further study the differences. Harnessing the different deep learning techniques from existing literature were compared using five iterative tests to determine the mean accuracy and the effects thereof. The results showed a Custom Architecture gave better results for the Chromagram, Spectrogram, and Me-Spectrum images and the VGG-16 architecture gave the best results for the MFCC image feature. This paper contributes to further assisting forensic investigators in differentiating between synthetic and real voices. © 2023 The Authors. Published by Elsevier B.V.

关键词： Cloning

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Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

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computers, Materials & Continua 2022年第2期70卷 2097-2111页

作者： R.H.Aswathy P.Suresh Mohamed Yacin Sikkandar S.Abdel-Khalek Hesham Alhumyani Rashid A.Saeed Romany F.Mansour Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare *** gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile *** Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the *** current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as *** steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and ***,the IoT gadgets are utilized in the collection of a patient’s health *** proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed *** application of FPA helps in tuning the DNN parameters for better classification *** simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD *** results were examined under different *** simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.

关键词： Deep learning chronic kidney disease IoT cloud computing feature selection classification

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A Comparative Analysis of Goal-Oriented Requirements Engineering and Model-Based Systems Engineering Frameworks for Managing Requirements of Systems of Systems 25

A Comparative Analysis of Goal-Oriented Requirements Enginee...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Alhajhassan, Suhair Murrar, Samer Samhan, Ahmad Odeh, Mohammed Elrashidi, Ali Applied Science Private University Software Engineering Department Amman Jordan Applied Science Private University Computer Science Department Amman Jordan Zarqa University Software Engineering Department Zarqa Jordan Global Academy for Digital Health Bristol United Kingdom University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems Engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs);GORE-based approach to Energy Management Systems (EnMS);Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services;GORE and reference architecture approach for microgrid systems;and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems. © 2024 IEEE.

关键词： Conceptual design

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