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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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computer Modeling in engineering & Sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Madapatha, Savin Fernando, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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Longitudinal Evaluation of Casual Puzzle Tablet Games by Older Adults

Longitudinal Evaluation of Casual Puzzle Tablet Games by Old...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Chaudhry, Beenish Moalla Islam, Muhammad Usama Chawla, Nitesh Vinay School of Computing and Informatics University of Louisiana Lafayette United States Department of Computer Science and Engineering University of Notre Dame United States

ISBN: (纸本)9798400705830

Despite growing interest in mobile games for older adults, there is limited exploration of older adults’ gaming behaviors, perceptions, and experiences as they engage with casual puzzle games over a period of time. To address this, we conducted a 9-month study with 20 older adults, examining training needs, in-situ experiences, and preferences. Participants were trained on tablet PCs and ten selected games. During the study, participants documented their experiences and attended technology workshops. Gaming behaviors were logged and analyzed using descriptive and inferential statistics, revealing patterns and statistically signifcant diferences in play frequency and duration over the course of the study. Thematic analysis identifed facilitators and barriers to engagement such as customization, co-play experiences, and health issues. Based on these fndings, we recommend incorporating educational elements, enhancing user control, leveraging identity and nostalgia, supporting social interactions, designing for tangible interaction, and emphasizing the importance of learning aids. Future research should test the efectiveness of these recommendations in increasing older adults’ engagement with casual games. © 2024 Copyright held by the owner/author(s).

关键词： Personal computers

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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Use of Blockchain for Fake Product Detection 5

Use of Blockchain for Fake Product Detection

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Sunil Vani, M. Sowmya Pavithra, N. Chandana, R. Jhansi, N. School of Computing Department of Computer Applications AP Tirupati India Sree Vidyanikethan Engineering College Department of Computer Applications AP Tirupati India

ISBN: (纸本)9798350383522

Products that are counterfeit pose a serious threat to both consumers and businesses. Our suggested method makes use of blockchain technology to establish an open and trustworthy platform for tracking phone goods all the way through the supply chain. Each product is assigned a unique digital ID stored on the blockchain, along with origin and production details. Stakeholders can scan the ID to verify product integrity. IoT and machine learning enhance identification by providing real-time data and analyzing patterns. Suspected counterfeit cases are flagged for investigation. This blockchain-based system protects brand reputation, revenue, and enables informed purchases. Blockchain's immutability prevents tampering, reducing fake goods prevalence. © 2024 IEEE.

关键词： Blockchain Fake Production Detection

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