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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Fatima Faridoon Raja Hashim Ali Zain Ul Abideen Nazia Shahzadi Ali Zeeshan Ijaz Usama Arshad Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis.

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning

Exploiting Partial Observability and Optimized Simple State ...

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Frontiers of Information Technology (FIT)

作者： Danyaal Mahmood Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent’s performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent’s decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game.

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A speech-driven pupil response system with affective highlight by virtual lighting

A speech-driven pupil response system with affective highlig...

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作者： Sejima, Yoshihiro Kawamoto, Hiroki Sato, Yoichiro Watanabe, Tomio Faculty of Informatics Kansai University 2-1-1 Ryozenji-cho Takatsuki-shi Osaka569-1095 Japan PEOPLE SOFTWARE CORPORATION 1-7-2 Achi Kurashiki-shi Okayama710-0055 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Soja-shi Okayama719-1197 Japan

In face-to-face communication, talkers perceive various affects and emotions of other talkers as the eye-impressions such as highlight or dilated pupil. In the previous studies, focusing on the eye-impressions in social robots, various expression methods such as changing the LED color or blinking the LED were proposed and developed. However, it is difficult to generate the affective expressions with the highlight that has an optical reflection characteristic on the surface of the eyeball by using the LED. Therefore, in order to enhance the eye-impressions of social robots, it is expected to develop an expressible method with the affective highlight. In this study, we proposed an affective highlight to enhance the eye-impressions of social robots. This method introduces a virtual lighting in virtual space and can generate rich affective expressions by controlling various parameters of the virtual lighting such as brightness or colors. In addition, we developed a speech-driven pupil response system that applied the affective highlight to the pupil response interface which has been developed. This system can generate the affective expressions by the dilation of pupil response that is synchronized with the talker’s speech as well as the highlight expression with talker’s intentional input. The effectiveness of the developed system with the affective highlight was demonstrated by sensory evaluations. © 2022 The Japan Society of Mechanical Engineers.

关键词： Light emitting diodes

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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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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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Abiodun E. Onile Juri Belikov Eduard Petlenkov Yoash Levron Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening ‘knowledge-action gap’. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user’s energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction.

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Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort.

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data systems

Towards Eco-Sustainability and Green Analytics Model to Meas...

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International Conference on Information Management (ICIM)

作者： Yusmadi Yah Jusoh Rusli Abdullah Rozi Nor Haizan Nor Jamaiah Hj. Yahaya Shireen Muhammad Aishwharya Raani Arunachalam Dept. of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Communication Visual Art and Computing Universiti Selangor (UNISEL) Selangor Malaysia

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS.

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