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Human Emotions Classification Using EEG via Audiovisual Stimuli and Ai

computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems 20

Robust and Reliable Liveness Detection Models for Facial Rec...

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20th international Conference on Frontiers of information Technology, FiT 2023

作者： Anjum, Haris Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali ijaz, Ali Zeeshan Siddique, Abu Bakar imad, Muhammad Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

ISBN: (纸本)9798350395785

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model's accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system. © 2023 iEEE.

关键词： Feature extraction

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A Highly Secured image Encryption Scheme using Quantum Walk and Chaos

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computers, Materials & Continua 2022年第10期73卷 657-672页

作者： Muhammad islam Kamran Muazzam A.Khan Suliman A.Alsuhibany Yazeed Yasin Ghadi Arshad Jameel Arif Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi15551UAE Institute for Energy and Environment University of StrathclydeGlasgowG11XQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)*** have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure *** of the classical random walk,this paper proposes the quantum walk to achieve high image *** random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave *** proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity *** experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally *** the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACi)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively.

关键词： Cryptography chaotic maps logistic map quantum walk security

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Bibliometric Study of Artificial intelligence and Semiconductor Manufacturing industry 15

Bibliometric Study of Artificial Intelligence and Semiconduc...

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15th international Conference on information and Communication Technology Convergence, iCTC 2024

作者： Won, Koohee K. Lee, Minyoung Park, Eunil Samsung Electronics Suwon Korea Republic of Sungkyunkwan University Department of Semiconductor and Display Engineering Suwon Korea Republic of Sungkyunkwan University Department of Applied Artificial Intelligence Seoul Korea Republic of Jaume I University Department of Computer Science and Engineering Castellon Spain

ISBN: (纸本)9798350364637

Artificial intelligence (Ai) has already changed the industry and is expected to bring more changes. Furthermore, the semiconductor manufacturing area, one of the extremely high-tech industries, is attempting to increase the production efficiency of the manufacturing industry based on a huge amount of data generated from semiconductor production equipment and also attempting to design production equipment through Ai. To understand the trend of these changes, we searched for various keywords including semiconductor manufacturing and Ai, and constructed a dataset through Scopus and Web of science, which are prominent databases, and limited the period to the period from 2020 to the present. We analyzed 1,498 publications from Scopus, 500 publications from Web of science, and a total of 1,579 publications. The number of research publications increased every year, which shows that research on semiconductor manufacturing and Ai is becoming more active with the increasing number of publications, and China and South Korea are the countries that produced the most publications. The main publication sources are the Proceedings of SPiE and iEEE Transactions on Semiconductor Manufacturing, and the keyword co-occurrence graph shows that there are attempts to apply Ai in various fields of semiconductor manufacturing. © 2024 iEEE.

关键词： Smart manufacturing

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ioT Devices Authentication Using Artificial Neural Network

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computers, Materials & Continua 2022年第2期70卷 3701-3716页

作者： Syed Shabih Ul Hasan Anwar Ghani ikram Ud Din Ahmad Almogren Ayman Altameem Department of Computer Science and Software Engineering International Islamic University Islamabad45000Pakistan Department of Information Technology The University of Haripur22620HaripurPakistan Chair of Cyber Security Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Chair of Cyber Security Department of Natural and Engineering SciencesCollege of Applied Studies and Community ServicesKing Saud UniversityRiyadh11543Saudi Arabia

User authentication is one of the critical concerns of information *** tend to use strong textual passwords,but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile *** passwords are slightly unprotected and are easily *** attacks include dictionary,shoulder surfing,and brute *** passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of *** paper proposes a Process-based Pattern Authentication(PPA)system for internet of Things(ioT)devices that does not require a server to maintain a static password of the login *** server stores user’s information,which they provide at the time of registration,i.e.,the R-code and the symbol,but the P-code,i.e.,the actual password,will change with every login attempt of *** this scheme,users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern,and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network(ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing *** experimental purposes,PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and *** experiments show that the system has an effect of 5.03%of the False Rejection Rate(FRR)and 4.36%of the False Acceptance Rate(FAR),respectively.

关键词： implicit authentication behavioral authentication artificial neural network processed pattern authentication

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NLSP: A novel lattice-based secure primitive for privacy-preserving smart grid communications

NLSP: A novel lattice-based secure primitive for privacy-pre...

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作者： Bao, HaiYong Xu, Zheng Hong, HaiBo Kong, QingLei Qian, HaiFeng Software Engineering Institute East China Normal University Shanghai China School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China Institute of Space Science and Applied Technology Harbin Institute of Technology Shenzhen China

As the new generation of power scheme, smart grid is proposed to overcome the shortcomings of traditional systems, such as low efficiency and reliability. in this article, a novel lattice-based secure primitive for privacy-preserving smart grid communications is proposed, which has the remarkable characteristics, such as scalable multi-dimensional fine-grained power data structure and differential privacy security. First, combining with the lattice-based data encryption technology, while effectively resisting quantum attacks, the method of simultaneous processing of multi-dimensional data is innovated. Second, through combining the additive homomorphism of the lattice-based cryptosystem and the Chinese remainder theorem, the data aggregation mechanism that can directly perform homomorphic operations on compressed ciphertext is constructed. Thanks to the above innovative design ideas, the proposed scheme not only significantly improves the efficiency of data communication and processing, greatly reduces the computational cost of the intermediate entity, but also realizes the data confidentiality and information privacy. Finally, observing the decentralized topology of communication nodes in the typical cyber-physical system of smart grid, the localized differential privacy technology is leveraged to optimize and balance the utility, security, and efficiency of differential privacy. Extensive performance evaluations are conducted to illustrate that the proposed scheme outperforms the state-of-the-art similar schemes in terms of computation complexity and communication cost. © 2022 John Wiley & Sons, Ltd.

关键词： Smart power grids

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K-Banhatti invariants Empowered Topological investigation of Bridge Networks

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computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th international Conference on Advances in Computing and Data sciences, iCACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. in order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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A Review of Algorithms's Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms's Complexities on Different Valued So...

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2023 international Conference on iT and industrial Technologies, iCiT 2023

作者： Shabbir, Attia Majeed, Asad iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. in this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. it was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 iEEE.

关键词： Computational complexity

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A Fused Machine Learning Approach for intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** intrusion detection scheme empowered with a fused machine learning technique(iDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed iDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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