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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Yongsatianchot, Nutchanon Marsella, Stacy Faculty of Engineering Thammasat School of Engineering Thammasat University Bangkok Thailand Khoury College of Computer Science and Department of Psychology Northeastern University Boston United States

ISBN: (纸本)9789819606948

Large Language Models (LLMs) have shown impressive capabilities across various fields, including across social sciences. While research has explored LLMs’ emotion capabilities, studies on coping mechanisms are limited. This work explored LLMs’ ability to represent emotion-focused coping, specifically updating beliefs and goals in response to emotional events. Coping has broad influences on physiological, cognitive, and social processes. We conducted two experiments on leading LLMs: a free-choice paradigm and a hurricane scenario. In the first experiment, LLMs did not exhibit preference changes between easy and hard choices, unlike humans. In the second experiment, LLMs showed no difference in hurricane perceptions based on evacuation decisions. Our findings suggest that current LLMs do not change or predict changes in beliefs and goals similarly to humans when coping with emotions, highlighting their limitations in modeling and predicting coping behaviors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coping Emotion Large Language Models

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A Machine Learning Approach in Garbage Collection and Monitoring with IoT Sensors 6

A Machine Learning Approach in Garbage Collection and Monito...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Dhanushya, S. Chitra, K. Swetha, S. Tharageswari, K. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

The greatest challenge people face today is garbage management and disposal. As the world is driven by technology, there are still small villages in India where waste is still disposedmanually by humans. As humans remove waste, it is frequently seen spilled in unexpected places, causing health problems and spreading deadly diseases. All of this occurs due to a lack of information about waste management anda lack of communication with those involved in the process. By considering this fact, waste management can be accomplished efficiently by segregating garbage and using it to produce compost. This research study has proposed a smart waste management system by integrating IoT sensors by checking the level of the bin used, sending GPS signals to the municipality and giving alert message to the authority in case if there is any production of poisonous gas from the garbage bin. The proposed method is a sustainable andreliable waste disposal and management methoddeployedin the state. The data collection for the proposed system can be leveraged to further plan the most efficient ways to lower waste mishandling and ensure a clean environment. In order to perform this process, machine learning algorithm is incorporated in the data testing and training process to get higher accuracy. The method adopted can also be used for monitoring and segregating the waste in future. © 2023 IEEE.

关键词： Bins

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Transforming Homes into Smart Havens: Advanced Control with NodeMCU and Android Technology 4

Transforming Homes into Smart Havens: Advanced Control with ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Selvarathi, C. Santhi, P. Deepa, K. Shri, G. Ramya Visali, C. Abirami, T. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamilnadu Chennai600 062 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirapalli India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Thalavapalyam639113 India Department of Information Technology Tiruchengode Namakkal637 205 India 638060 India

ISBN: (纸本)9798331508845

Creating groundbreaking solutions that benefit the populace is imperative in the modern era. The Internet of Things (IoT) serves as a trailblazing sector, fostering the development of contrivances automating an array of tasks. The IoT encompasses an interlinked network of cyber-connected contrivances executing functions with minimal human intervention. Capitalizing on IoT principles, this exploration endeavors to contrive a device aiding individuals grappling with mobility impediments. The envisioned gizmo consists of two primary sectors: a hardware module featuring a channel relay, Arduino, and an astute luminescent bulb, and a software element encompassing cloud and IoT platforms alongside a purpose-built app. The chief function of this contraption is to discern specific vocalizations in an individual's utterances, effectuating or deactivating the astute contraption to institute an intelligent habitation. In the initial phase, a luminous bulb will serve as a demonstration, employing for app origination. The distinctive aspect of this program lies in its vocalization-receptive microphone, with Bluetooth and Wi-Fi chips augmenting transmission velocity between the luminescent bulb and the mobile device. Meticulous attention will be accorded to auditory sensitivity and expeditious data transmission, with potential integration of diverse contrivances like ventilators and temperature-regulating apparatus into the envisioned gizmo. Furthermore, contemplation extends to a fortified safety attribute ensuring singular response to a particular vocal identity. © 2024 IEEE.

关键词： Luminescent devices

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Robust Particle Swarm Optimization Algorithm for Modeling the Effectof Oxides Thermal Properties on AMIG 304L Stainless Steel Welds

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computer Modeling in engineering & sciences 2024年第11期141卷 1809-1825页

作者： Rachid Djoudjou Abdeljlil Chihaoui Hedhibi Kamel Touileb Abousoufiane Ouis Sahbi Boubaker Hani Said Abdo Department of Mechanical Engineering College of Engineering in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 655Al-Kharj16273Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Laboratory of Mechanics of Sousse(LMS) National Engineering School of SousseUniversity of SousseSousse4054Tunisia Center of Excellence for Research in Engineering Materials(CEREM) King Saud UniversityP.O.Box 800Al-Riyadh11421Saudi Arabia Department of Metallurgical and Materials Engineering Faculty of Petroleum and Mining EngineeringSuez UniversitySuez43512Egypt

There are several advantages to the MIG(Metal Inert Gas)process,which explains its increased use in variouswelding sectors,such as automotive,marine,and construction.A variant of the MIG process,where the sameequipment is employed except for the deposition of a thin layer of flux before the welding operation,is the AMIG(Activated Metal Inert Gas)*** study focuses on investigating the impact of physical properties ofindividual metallic oxide fluxes for 304L stainless steel welding joint morphology and to what extent it can helpdetermine a relationship among weld depth penetration,the aspect ratio,and the input physical properties ofthe *** types of oxides,TiO_(2),SiO_(2),Fe_(2)O_(3),Cr_(2)O_(3),and Mn_(2)O_(3),are tested on butt joint design withoutpreparation of the edges.A robust algorithm based on the particle swarm optimization(PSO)technique is appliedto optimally tune the models’parameters,such as the quadratic error between the actual outputs(depth and aspectratio),and the error estimated by the models’outputs is *** results showed that the proposed PSOmodel is first and foremost robust against uncertainties in measurement devices and modeling errors,and second,that it is capable of accurately representing and quantifying the weld depth penetration and the weld aspect ratioto the oxides’thermal properties.

关键词： Activated metal inert gas welding stainless steel activating flux oxides’thermal properties particle swarm optimization

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An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms 2

An Analytical Approach to Fraudulent Credit Card Transaction...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Yuhes Raajha, R.M. Kavin, A. Rajkumar, D. Reshma, R. Santhosh, R. Mekala, N. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350346640

Technology and the revolution in communication have increased the popularity of digital money usage. Most of the monetary transactions currently take place digitally. It is more convenient and increases the ease for the user. But one major problem in digital money and credit card usage is security. With the increase in credit card usage, security issues increase correspondingly. Many studies and research work are going on to avoid and prevent such practices from taking place. Moreover, various studies on real-international credit scorecard statistics are attributable to confidentiality issues. This paper focuses on current credit card fraud practices and fraud detection methods implemented in real time. Different ML algorithms like fuzzy-based SVM (FSVM), random forest (RF), logistic regression (LR), and support vector machine (SVM) for fraudulent transaction detection on the dataset collected from credit card users have been used to classify legitimate and fraudulent transactions. The comparative analysis of the credit card fraud detection scheme using these classification models was performed with precision, accuracy, sensitivity, and specificity. The comparative analysis outcomes showed that the highest performance was given by the FS VM over other algorithms with an accuracy of 98.61%. © 2023 IEEE.

关键词： Support vector machines

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An Efficient Attack Detection Framework in Software-Defined Networking using Intelligent Techniques 3

An Efficient Attack Detection Framework in Software-Defined ...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Hassan, Heba A. El-Din Hemdan, Ezz Shokair, Mona El-Samie, Fathi E. Abd El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf32952 Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Communication Engineering Menouf32952 Egypt Prince Sultan University Security Engineering Laboratory Department of Computer Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Recently, Software-Defined Networking (SDN) architecture has offered great benefits due to the separation between the control and network elements such as routers and switches. Unfortunately, the enormous growth of attacks hinders the wide adoption of SDN. Intrusion Detection Systems (IDSs) are used as significant tools to detect and mitigate network attacks of anomalous nature. Several Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs), have been utilized for building IDSs in cyber security in recent years, because they achieve the desired detection results. Nonetheless, little work has been introduced on IDSs in SDN systems. Hence, this paper presents an efficient framework based on intelligent techniques for predicting attacks in SDN systems. This work focuses on the early detection of abnormal attacks existing in an SDN environment. When malicious traffic is identified in an SDN topology, the Artificial Intelligence (AI) module employs Machine Learning (ML) or DL models to identify and stop the attack source. The architecture presented in this research allows for comparing several ML and DL classification techniques that can be used to identify different sorts of network attacks. The proposed framework is tested on the InSDN dataset using several learning models like Random Forest (RF), AdaBoost (AB), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Logistic Regression (LR) classifiers. In addition, DL models such as Deep CNN and Long Short Term Memory (LSTM) are considered. The results demonstrate that the proposed Deep CNN model for multi-class attack data classification achieves the highest accuracy of 99.85% compared to RF, AB, KNN, NB, DT, LR, and LSTM classifiers with accuracy levels of 95%, 88 %, 97%, 93 %, 90 %, 98%, and 88.31 %, respectively. © 2023 IEEE.

关键词： Software defined networking

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A CNN-based Canny Edge Detection Approach for Car Scratch Detection 6

A CNN-based Canny Edge Detection Approach for Car Scratch De...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Sasikumar, A. Sathyanarayanan, M. Sriayapppan, A.N. Santhosh, R. Reshma, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

Image-based vehicle protection processing is a significant technological scope with a huge degree of computerization. In this research work, the advancement and modeling of a novel design proposal to identify scratches in vehicles using image processing and a deep learning is performed. This works also present and android application by implementing the proposed model for car scratch detection. As the image dataset included the images of the vehicle are captured in a different view for the image processing and detection, scratch in the vehicles can be detected using a canny edge detection scheme with different parts on the surface of the vehicle and it reduces the noise of the image. The convolutional neural network (CNN) is applied here to classify scratch zone and non-scratch zone in the surface area of the vehicle. The experimental result shows that the proposed system has increased detection accuracy with an average of 97.31% and reduces the noise image density up to 12.76% comparative to the existing models. © 2023 IEEE.

关键词： Convolutional neural networks

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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A Secure and Efficient Product Analysis for Trust based E-Commerce Application 2

A Secure and Efficient Product Analysis for Trust based E-Co...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Mugil, P. Jeevanantham, C. Yadav, Yerragolla Nikhil Nandha Harikaran, M. Dhanapal, R. Faculty of Engineering Department of Computer Science and Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9781665491990

In current situation, e-commerce is considered essential and a growing business in day today life. People are buying the product based on deals and offers provided in the ecommerce application. Customer does not know about the quality and authenticity of the product, and sometimes the seller does not meet the customer requirements. To avoid this situation, a Secure Trust Monitoring KNN (STRUMKNN) ecommerce model has been implemented in this research. This model consists of a customer trust reputation system based on feedback comments and rating system given by each client. This model helps customer to find the good seller in ecommerce sites based on trust weight to find the product quality and product delivery. In the proposed model, two techniques such as word separation method and sentiment analysis are employed for providing trust profile to the customer. Here, based on the key pair of words either positive or negative given by a customer in the comments section, the suggestion is given to the buyer to order the product on trust profile. Sentiment analysis helps to identify if the customer ratings given by the user are accurate or not, based on the ratings and trust. The experimental results prove that the sentimental assessment used in the suggested model provides customers a successful way of finding the product and increases the confidence of e-commerce application users. © 2023 IEEE.

关键词： Quality of service

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Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

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computers, Materials & Continua 2023年第2期74卷 3607-3625页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)SkudaiJohorMalaysia Department of Electronics and Electrical Engineering Faculty of EngineeringInternational University of AfricaKhartoum12223Sudan Department of Communication Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu Pahat86400JohorMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond *** random movement of mobile terminals in the indoor environment is a challenge in the VLC *** model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)*** optimized optical attocells were examined and compared with previous *** novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 *** bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,*** optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,*** the results of the proposed model are optimal,it is suitable for standard office and room model applications.

关键词： Visible light communication(VLC) optical attocell received power signal-to-noise ratio(SNR) bit error rate(BER) coefficient of variation(CV)

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