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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Ayman, Hazem Bassem, Madonna Madkour, Malak Fawzi, Mariam Amr, Nada Yousry, Youmna Elgayar, Ziad Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem. © 2023 IEEE.

关键词： Gaussian distribution

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A Code-Switched Arabic-English Sentiment Analysis Approach Based on Deep-Learning 3

A Code-Switched Arabic-English Sentiment Analysis Approach B...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Almasah, Tasneem S. Ebrahim, Gamal A. Abdelaal, Marwa A. The British University in Egypt Faculty of Engineering Electrical Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Sadat Academy for Management Sciences Faculty of Computers and Information Computer Science Department Cairo Egypt

ISBN: (纸本)9798350306231

Sentiment analysis within Online Social Networks (OSNs) becomes a major challenge. Mainly, because of the large amount of data on social networks and the mix of different languages that can be used in these environments. Due to the lack of existing research and datasets on code-switched Arabic-English sentiment analysis, this study creates a new dataset collected from various online social networks. The data is subjected to several preprocessing steps before being analyzed using various deep learning architectures. Moreover, a thorough evaluation of the preprocessing techniques and the performance of the deep learning models have been conducted. This is achieved using comprehensive Twitter datasets in Arabic and English. The study reveals that individual deep learning architectures, specifically, Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (Bi-LSTM), and LSTM demonstrated superior results compared to their hybrid counterparts. Notably, CNN emerged as the most accurate model with an accuracy of 83%, followed closely by Bi-LSTM at 82.3%. Additionally, the paper presents the deployment of an automated machine learning tool, which is Auto-Keras. Furthermore, it designs an optimized sentiment analysis model that aims to enhance accuracy. This approach leads to an increase in the model accuracy to 86%. © 2023 IEEE.

关键词： Sentiment analysis

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A Study on Artificial Intelligence-based Natural Language Processing for Online Shopping Services 6

A Study on Artificial Intelligence-based Natural Language Pr...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Parthipan, N. Ramar, K. Prasath, Ragu S. 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

As a result of the developments in the digital age, communication has become the primary route for modern business. Business discussions, profiles, seminars, transactions, and agreements were all conducted in person in the past, but today practically everything is done digitally. The goal of this work is to investigate how text-object connections made by artificial intelligence help NLP and AI better meet human requirements. Additionally, this work considers how modern applications like NLP and AI integrated with IoT services have altered online shopping environments. Online shopping service providers have created sophisticated NLP systems that take human demands and instantaneously satisfy them through text, phone conversations, smart records, and chatbots as communication takes centerstage in the commercial environment. This work modeled the artificial intelligence-based natural language processing framework for online shopping services and evaluated using support vector machine (SVM) classification algorithm and a hybrid Naive Bayes - Hidden Markov model (NB-HMM) algorithm. The experimental analysis shows the better performance when the model use SVM for classification over NB-HMM with the average accuracy of 94.3% and F-score of 97.5%. © 2023 IEEE.

关键词： Support vector machines

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Exploring Large Language Models’ Ability to Imitate Coping’s Influence on Beliefs and Goals 17th

Exploring Large Language Models’ Ability to Imitate Coping...

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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 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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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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