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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Gopala Krishnan, D. Diviya, M. Jyoshika Shri, K. Subramanian, M. Amrita School of Computing Department of Computer Science and Engineering Chennai India St Joseph's College of Engineering Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350379884

The increasing popularity of Internet of Medical Things (IoMT) devices, like wearable sensors, has greatly improved patient care by allowing continuous monitoring and real-time data transfer to the cloud. However, this progress also raises security concerns, as the data sent from these devices can be at risk of tampering or hacking during transmission. Keeping this sensitive medical data safe is crucial for patient safety. To address these issues, a fused CNN and LSTM are developed to check whether the data from wearable IoMT devices has been altered or attacked during transmission to the cloud. The CNN part of the model helps to pull out important features from the data, while the LSTM part looks for patterns to spot any signs of tampering. The model uses two convolutional layers each of which is followed by a MaxPooling layer. Our model, tested on a large dataset, achieved an accuracy of 97.06%, showing its ability to protect the integrity of transmitted medical data. © 2024 IEEE.

关键词： Network intrusion

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Challenges and Opportunities in Healthcare and Industrial IoT: A Comparative Analysis 1

Challenges and Opportunities in Healthcare and Industrial Io...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Berjis, Zaid Ameen, Siddeeq Y. Al-Jammas, Mohammed H. Electrical and Computer Engineering Department College of Engineering University of Duhok Duhok Iraq Energy Engineering Department College of Technical Engineering Duhok Polytechnic University Duhok Iraq Computer and Information Engineering Department College of Electronics Ninevah University Mosul Iraq

ISBN: (纸本)9798331533557

The Internet of Things (IoT) has changed many industries by enabling smart devices to transmit data, operate autonomously, and interact in real-time. Among its most prominent applications are in healthcare and industrial sectors, where IoT is classified into two classes: massive Machine-Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). While mMTC devices prioritize low energy consumption and can handle large-scale deployments with minimal data rates, URLLC devices are focused on mission-critical applications requiring ultra-low latency and high reliability. This paper examines the challenges and opportunities of IoT implementation in healthcare and industrial sectors, focusing on interoperability, data security, and scalability issues. The objective is to provide actionable solutions for the effective integration of IoT technologies. The investigation shows that in healthcare, mMTC is utilized for non-critical monitoring, while URLLC supports critical tasks like remote surgery. In industrial settings, mMTC improves processes like supply chain management, while URLLC enables automation and real-time control of machinery. Despite these opportunities, challenges such as data privacy, security, interoperability, and reliability remain. Addressing these issues is crucial for unlocking the full potential of IoT in healthcare and industrial sectors. © 2024 IEEE.

关键词： Data privacy

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Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

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computer Systems Science & engineering 2023年第6期45卷 2351-2362页

作者： S.Aanjanadevi S.Aanjankumar K.R.Ramela V.Palanisamy Department of Computer Applications Alagappa UniversityKaraikudi630003TamilnaduIndia Department of Computer Science and Engineering Sri Raaja Raajan College of Engineering and TechnologyAmaravathipudur630301TamilnaduIndia Department of Electrical and Electronics Engineering Ultra College of Engineering and TechnologyMadurai625020TamilnaduIndia

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major *** challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database *** describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification *** this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential *** proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary *** Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further *** Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while *** codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and *** main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify *** system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.

关键词： FACNN biometric cryptography AI FAR FRR EER

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Cryptocurrency Portfolio Optimization by Neural Networks

Cryptocurrency Portfolio Optimization by Neural Networks

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Nguyen, Quoc Minh Tran, Dat Thanh Kanniainen, Juho Iosifidis, Alexandros Gabbouj, Moncef Tampere University Department of Computing Sciences Finland Aarhus University Department of Electrical and Computer Engineering Denmark

ISBN: (纸本)9781665430654

Many cryptocurrency brokers nowadays offer a va-riety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. The proposed algorithm constructs a portfolio that contains a pair of negatively correlated assets. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy. Extensive experiments were conducted using data collected from Binance spanning 19 months to evaluate the effectiveness of our approach. The backtest results show that the proposed algorithm can produce neural networks that are able to make profits in different market situations. © 2023 IEEE.

关键词： cryptocurrency decision making Deep learning financial engineering portfolio optimization

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Research on Multi-Objective-Multi-Elevator Coordinative Strategy based on Neural Network 4

Research on Multi-Objective-Multi-Elevator Coordinative Stra...

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4th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Zhang, Wei Chen, Ziyu Gao, Minyi L., Zou Z., Pan Department of Electrical and Computer Engineering Nanfang College Guangzhou China

ISBN: (纸本)9781510666412

With rapid development of industry, more and more attention has been paid to the effective control of elevator in high-rising buildings. This paper studied the elevator control problem, proposed a multi-objective-multi-elevator cooperative scheduling strategy based on the BP neural network, constructed the BP neural network model with four dimensional indexes such as time, energy consumption, comfort level and efficiency, established the elevator's dispatching decision model and obtained the optimal strategy in each period of a day. Finally, the accuracy and feasibility of the model are verified by taking six ten-floor elevators as the specific control objects. The verification results shows that the proposed algorithm is accurate, efficient and flexible. © 2023 SPIE.

关键词： Neural networks

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Convolutional neural network-based image watermarking using discrete wavelet transform

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International Journal of Information Technology (Singapore) 2023年第4期15卷 2021-2029页

作者： Tavakoli, Alireza Honjani, Zahra Sajedi, Hedieh Department of Computer Engineering School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Computer Science School of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran

With the growing popularity of the Internet, digital images are used and transferred more frequently. Although this phenomenon facilitates easy access to information, it also creates security concerns and violates intellectual property rights by allowing illegal use, copying, and digital content theft. Using watermarks in digital images is one of the most common ways to maintain security. Watermarking is proving and declaring ownership of an image by adding a digital watermark to the original image. Watermarks can be either text or an image placed overtly or covertly in an image and are expected to be challenging to remove. This paper proposes a combination of convolutional neural networks (CNNs) and wavelet transforms to obtain a watermarking network for embedding and extracting watermarks. The network is independent of the host image resolution, can accept all kinds of watermarks, and has only 11 layers while keeping performance. Performance is measured by two terms;the similarity between the extracted watermark and the original one and the similarity between the host image and the watermarked one. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Convolutional neural networks Neural networks Watermarking Wavelet transform

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A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 789-800页

作者： Hongjun Gao Zhenyu Liu Youbo Liu Lingfeng Wang Junyong Liu College of Electrical Engineering Sichuan UniversityChengdu 610065China Department of Electrical Engineering and Computer Science University of Wisconsin-MilwaukeeMilwaukeeWI 53211USA

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of *** this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness *** the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust *** worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind *** whole two-stage model is solved by the column-and-constraint generation(CCG)***,case studies are conducted to show the performance of the proposed model and various *** Terms-Data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.

关键词： Data-driven methods distributionally robust optimization urban integrated energy system wind power

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Novel add-drop filter based on serial and parallel photonic crystal ring resonators (PCRR)

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Journal of Optical Communications 2024年第2期45卷 235-247页

作者： Fakhruldeen, Hassan Falah Al-Asady, Heba Abdul-Jaleel Mahinroosta, Tayebeh Sohrabi, Foozieh Hamidi, Seyedeh Mehri Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-sadiq University Baghdad Iraq Electrical Engineering Department College of Engineering University of Kufa Kufa Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf Iraq Magneto-Plasmonic Lab Laser and Plasma Research Institute Shahid Beheshti University Tehran*** Iran

Photonic crystal ring resonators (PCRR) as momentous candidates for future photonic crystal integrated circuits (PCICs) draw worldwide attention. In this paper, different configurations are proposed based on single, parallel, and serial PCRRs. To be precise, the different coupling lengths and alignments have been discussed in double and triple PCRRs in parallel and serial configurations to achieve the highest efficiency concerning the desired applications such as an add-drop filter (ADF) and a power splitter. Moreover, in the achieved optimum double and triple PCRRs, the effect of coupling radius change has been discussed. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Photonic crystals

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