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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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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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Computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar Ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

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Extracurricular Environment for Electronics Packaging Knowledge Integration 46

Extracurricular Environment for Electronics Packaging Knowle...

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46th International Spring Seminar on Electronics technology, ISSE 2023

作者： Moisa, Cosmin Codreanu, Norocel Svasta, Paul Negrea, Cǎtǎlin Cenuşǎ, Mihai Ionescu, Ciprian Advanced Projects Software and Central Technologies SC Continental Automotive Romania SRL Timisoara Romania University Politehnica of Bucharest Faculty of Electronics Telecommunications and Information Technology Bucharest Romania Research and Development DarkNote Engineering Timisoara Romania SC Continental Automotive Romania SRL Iasi Romania

ISBN: (纸本)9798350334845

This paper describes a series of electronics packaging scale-up events proved to be prolific and full of maturity. The organizing structure and the topics are consistent, with pragmatic implications and transparency in the proposed solving with industrial conciseness and level of detail required. Given the high focus on functional and constructive details of the subjects revealed to the contestants (who are bachelor and master students), which are similar to industry requirements, it is worthy to understand, based on this paper, the basics of the environment, established three more than decades ago. The students who have participated proved their competitive potential and there is a high return on investment (ROI) in specialists and experts for the IEEE EPS community. By our active involvement, we hope to give to the next generations of hardware engineers the chance to apply their electronics knowledge in the vast field of the electronics industry. © 2023 IEEE.

关键词： E-learning

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Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation

arXiv

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arXiv 2024年

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Design, Modeling and implementation of Smart grid using PROTEUS and Arduino 1

Design, Modeling and implementation of Smart grid using PROT...

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1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2021

作者： Salem Ahmed, Nuredin Ali Shaari, Hala Emhemmed, Adel Saad University of Tripoli Faculty of Information Technology Software Engineering Deparment Tripoli Libya University of Tripoli Electrical and Electronics Engineering Department Tripoli Libya

ISBN: (纸本)9781665418560

Electricity is one of the fundamental necessities of human beings, which has many uses in our day to day life. It is used for different purposes like domestic, industrial and agricultural. The biggest challenge facing electricity distribution is data collection and meter reading. Right now, meter reading is collected manually which give scope for corruption and human error in reading, moreover the wastage of manpower and resources of utility. Prepaid Energy Meter has been implemented in several countries. In fact, the disadvantage of the system is the behavioral control of the users. Moreover, recharging should be carried out on the meter. The problem occurs when consumers leave their premises and electrical pulses are discharged. That's why we need a system to control the electrical pulse wherever they are. In this work, a prepaid energy meter was proposed, implemented and simulated using PROTEUS software. The system was designed using ATmega128 as a microcontroller and GSM technology is advancement over conventional energy meter, which enables consumer to effectively manage their electricity usage. Additionally, it evaluates the accuracy of voltage and current measured by means of this model. Our Suggested model of the prepaid power meter produces the lowest error compared to actual voltage and current. The proposed system replaces traditional meter reading methods and enables remote monitor and control the meter readings regularly not manually. Also, it alerts the consumer when the energy consumption exceeds above the set limit and alerts the utility company if there is any theft that might be happened. © 2021 IEEE.

关键词： Smart meters

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Unmasking The Artist: Discriminating Human-Drawn And AI-Generated Human Face Art Through Facial Feature Analysis 6

Unmasking The Artist: Discriminating Human-Drawn And AI-Gene...

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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Nguyen, Minh-Quang Ho, Khanh-Duy Nguyen, Hoang-Minh Tu, Canh-Minh Tran, Minh-Triet Do, Trong-Le University of Science VNU-HCM Faculty of Information Technology Ho Chi Minh City Viet Nam University of Science VNU-HCM Software Engineering Laboratory Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

In the 1990s, the world witnessed a revolutionary breakthrough in the realm of AI-generated art, where its applications surpassed mere visual effects. An ever-increasing number of AI-generating applications emerged, posing a critical challenge for many art contest organizations. These organizations stipulate that the artwork presented must result from human creativity without any help from AI-generated applications. This dilemma accentuates the urgency of drawing a clear distinction between AI-generated and human-made artwork, a task of paramount importance. After observing multiple works of art from AI and humans, the authors notice that the Gradient-Based feature can be utilized to differentiate images generated from different sources of generators. Therefore, the authors decided to create the dataset by collecting paintings from a state-of-the-art AI-generating art model, using algorithms for extracting features with multiple models for detecting AI-generated images and evaluating their performance. Solving this problem can help many art contest organizations as they require artists to create artwork rather than AI. © 2023 IEEE.

关键词： Computer vision

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iRetina: An Intelligent Mobile Application for the Visually Impaired in an Indoor Environment 16

iRetina: An Intelligent Mobile Application for the Visually ...

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16th IEEE International Conference on Industrial and information Systems, ICIIS 2021

作者： Labeeshan, A. Satharasinghe, S.A.R.L.P. Dhananjana, A.M. Rupasinghe, T.D. Samantha Rajapaksha, U.U. Haddela, Prasanna S. Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665426374

The Visually Impaired face a lot of problems while they carry out their day-to-day activities. Even though there are various ways that technologies have been used together to come up with a solution, there is still no proper, efficient, user-friendly, low-cost solution crafted just for them. Mobile Phones these days are beyond smartness, and they could be used to achieve so many things now compared to before. In this application, the camera of the mobile phone acts as the eyes for the Visually Impaired. The main purpose of this system is to intelligently navigate the visually impaired. This research covers some components namely, intelligent environmental identification by using the YOLOv4 object detection algorithm, calculation of distance by using the Inertial Measurement Unit (IMU) sensors, focusing, guiding and detecting the expiry date of packaged products, identifying and enhancing objects in a low-light environment. The communication between the system and the Visually Impaired will be a verbal voice communication through the microphones and the speakers of the mobile phone. The results presented show that the proposed application successfully achieves its goals by providing the required functionality. © 2021 IEEE.

关键词： Object detection

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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

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Computer Systems Science & engineering 2023年第10期47卷 375-391页

作者： Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa King Abdulaziz City for Science and Technology P.O Box 6086Riyadh 11442Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of AIML Aditya Engineering CollegeSurempallemAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

关键词： Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics

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Architecture for a Modular Temperature Detection System Used for Diabetic Foot Investigations

Architecture for a Modular Temperature Detection System Used...

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2022 International Semiconductor Conference, CAS 2022

作者： Serbanescu, Matei Nastase, Florin Draghici, Florin Pristavu, Gheorghe Catrina, Eduard Chiriac, Radu Brezeanu, Gheorghe University Politehnica of Bucharest Faculty of Electronics Telecommunications and Information Technology Romania Tremend Software Consulting Bucharest Romania 'Dr. I. Cantacuzino' Clinical Hospital Bucharest Romania University Politehnica of Bucharest Faculty of Mechanical Engineering and Mechatronics Romania

ISBN: (数字)9781665452557

ISBN: (纸本)9781665452557

Feet ulcerations represent a prevalent and severe complication of diabetes. This paper presents an architecture for a modular temperature gradient detection system. The solution allows scanning of the plantar regions as a key driver for an accurate identification of local inflammations, prior to ulcer development. It comprises two parallel strings of sensing modules, serially connected to a main board, which facilitates both data acquisition and data transmission to a computer. The system demonstrated measurement resolution as low as 0.05°C and response time of circa 60 seconds. The active mode current consumption (17 mA) enables the usage of a battery supply, increasing portability, safety and ease-of-operation. © 2022 IEEE.

关键词： Data acquisition

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