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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

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

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.

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The Effect of Balanced and Imbalanced Dataset on Voice Pathology Detection Using Online Sequential Extreme Learning Machine (OSELM)

The Effect of Balanced and Imbalanced Dataset on Voice Patho...

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Advanced Telecommunication and Networking Technologies (ATNT), IEEE International Conference on

作者： N. F. S. Md Sazihan N. M. Abdul Latiff N. N. N. Abd Malik S. Mashohor K. Alaghbari F. T. AL-Dhief Faculty of Electrical Engineering Universiti Teknolgi Malaysia UTM Johor Bahru Malaysia Dept. of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia

ISBN: (数字)9798350353501

ISBN: (纸本)9798350353518

Voice pathology detection is crucial for early diagnosis and treatment of vocal disorders. This research studies the effect of balanced and imbalanced datasets on the accuracy of voice pathology detection using Online Sequential Extreme Learning Machine (OSELM) on the Saarbruecken Voice Database (SVD). The dataset's balance has been identified as having a significant influence on the performance of the machine learning models. In this work, the voice signals from vowel /a/ of pathology and non-pathology classes are taken from SVD. The dataset is divided into two category which are balanced and imbalanced dataset. Then, the features of the voice signals are then extracted using Mel-Frequency Cepstral Coefficient (MFCC) and fed into classifier called OSELM. The result of balanced and imbalanced dataset is assessed for its accuracy. The results showed that the model achieved an accuracy of 66.25% with the balanced dataset compared to 48% for the imbalanced dataset. These findings highlight the importance of addressing dataset balance to enhance the reliability and effectiveness of voice pathology detection systems.

关键词： Pathology Accuracy Extreme learning machines Databases Machine learning Feature extraction Communications technology Telecommunication network reliability Mel frequency cepstral coefficient

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Improving the Efficiency of Smart Grid Scheduling: A Linear Regression Model with Meta Job Information

Improving the Efficiency of Smart Grid Scheduling: A Linear ...

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of electrical Engineering and information systems German Jordianian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using the number of CPUs, average CPU speed, and memory size, whereas a categorical predictor is built using the user ID, group ID, and executable ID. According to the findings, the prediction rates for categorical and continuous predictors are 61% and 1%, respectively. sixty times better than the earlier models, which used continuous variables as a foundational model to determine task complexity and weight. The effectiveness of the categorical predictor in enhancing a job scheduling problem is then evaluated by combining it with three suggested job scheduling strategies. The suggested algorithms incorporated metrics—predicted run time, waiting time, and resource requirement—to select the smallest jobs. According to the results, Algorithm 3 performs better than earlier models in both performance metrics. The variation in total execution time and average waiting time is 1.14 to 1.76 and 4.5, respectively compared to previous models, Additionally, Algorithms 1 and 2 demonstrating superior performance in every scenario.

关键词： Measurement Processor scheduling Computational modeling Linear regression Predictive models Prediction algorithms Smart grids

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Prediction and optimisation of electricity market clearing price in Turkey by using machine learning methods

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International Journal of Oil, Gas and Coal Technology 2025年第4期37卷 438-466页

作者： Ince, Murat Kabul, Ahmet Aksoy, Mesut Department of Computer Technologies Isparta University of Applied Sciences Isparta Turkey Mechanical Engineering in Technology Faculty Isparta University of Applied Sciences Isparta Turkey Energy Systems Engineering in Technology Faculty Isparta University of Applied Sciences Isparta Turkey

This research aims to reduce price instability in the market clearing price (MCP) in Turkey by estimating MCP using machine learning techniques based on production resource-based data. The model will balance market prices by shifting from a price-based to a resource-based approach, minimising the price of electricity units by decreasing imported energy production and increasing domestic and renewable energy production. Thus, in this study, the effect of MCP on electricity unit prices and forecast values until July 29, 2023, was compared. By using past year data between 2014 and 2022, the MCP price in 2023 is determined. As a result of artificial neural network prediction, the average MCP value for 2023 was revealed 85.9 USD. The best results were obtained with artificial neural network (ANN) (R2 = 0.8827, RMSE = 0.0309 and MAE = 0.0223). Also, the model predicts estimated 2023 energy production by incorporating real-time production values from energy resource production data. The performance indicators of the implemented forecasting methods increase efficiency in future production forecasts and contribute to accurate pricing in energy purchases. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Linear regression

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services 2

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

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

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

ISBN: (纸本)9798350328202

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. © 2024 IEEE.

关键词： Sentiment analysis

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SSVEP-BCI in Augmented Reality in Realistic Environment

SSVEP-BCI in Augmented Reality in Realistic Environment

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Bhuwit Chaiyarit Setha Pan-Ngum Pasin Israsena Arpa Suwannarat Apit Hemakom Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Biomedical Electronics and Systems Research Team National Electronics and Computer Technology Center Pathumthani Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

This study investigates Augmented Reality-based Steady-State Visually Evoked Potentials (AR-SSVEP) in Brain-computer Interface (BCI) systems, utilizing Extended Filter Bank Canonical Correlation Analysis (Extended-FBCCA) for SSVEP recognition optimization across various frequencies. The 10 Hz stimulus consistently achieves the highest recognition accuracy. Frequency-specific variations at 12 Hz, 13 Hz, and 15 Hz highlight nuanced SSVEP responses. White stimuli yield PC-SSVEP recognition ranging from 68.32% to 81.98%, slightly outperforming AR-SSVEP-Controlled (63.59% to 73.23%). Red stimuli impact AR-SSVEP-Controlled less than PC-SSVEP, with recognition ranges of 68.65% to 76.30% and 67.97% to 76.72%, respectively. Comparisons show a general slight outperformance of PC-SSVEP, with exceptions suggesting frequency-specific outcomes. AR-SSVEP-Controlled surpasses AR-SSVEP-Static, emphasizing the impact of a controlled visual environment. AR-SSVEP-Static recognition ranges from 33.49% to 54.59% for white stimuli and 36.15% to 53.04% for red stimuli. Subjects' movement in AR-SSVEP-Walk consistently lowers accuracy, ranging from 31.74% to 47.70% for white stimuli and 26.43% to 41.67% for red stimuli, with frequency-specific exceptions. Stimulus colors' complex effects underscore the requirement for stimulus color selections. Spatial distribution analysis reveals higher accuracy in the O-region. Overall, this study navigates the complex interaction between AR-SSVEP and environmental conditions. Future research may focus on developing algorithms for automatic configuration selection in Extended-FBCCA, ensuring adaptability to varying frequencies and real-world conditions.

关键词： Visualization Accuracy Frequency modulation Correlation Navigation Signal processing algorithms Color

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Performance Evaluation of High Gain 2×2 MIMO Microstrip Antenna Operating at 28 GHz 2

Performance Evaluation of High Gain 2×2 MIMO Microstrip Ant...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Ismail, Alyani Binti Rasid, M.F.A. Hamidon, Mohd Nizar B Abdullah, Raja Syamsul. A. Raja Shebani, Nafaa Mohamad Universiti Putra Malaysia Wireless and Photonics Networks Research Centre Department of Computer and Communication Systems Engineering Faculty of Engineering Serdang Malaysia Universiti Putra Malaysia Institute of Nanoscience and Nanotechnology Department of Electrical and Electronic Engineering Faculty of Engineering Serdang Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

In this paper, four configurations of 2×2 MIMO microstrip antenna operating at 28 GHz are proposed and simulated by computer Simulation Technology (CST) software. The antenna is designed on PTFE/Teflon substrate with dielectric constant of 2.1, loss tangent of 0.0002, and thickness of 1.58 mm. The performance of all proposed configurations of 2×2 MIMO antennas are evaluated based on the overall gain, envelope correlation coefficient (ECC), and diversity gain (DG). Moreover, the comparison between all cases in this work is done in order to choose the optimal configuration, and to prove the reliability of the design the best obtained case is compared with proposed antennas in published works. © 2022 IEEE.

关键词： Microstrip antennas

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Development of the Function Check Stability of Digital Media Receivers using Interference Mitigation Techniques

Development of the Function Check Stability of Digital Media...

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International Electrical engineering Congress (iEECON)

作者： Pontakan Lhuangchon Chaiyong Soemphol Department of Electrical and Computer Engineering Faculty of Engineering at Mahasarakham University Mahasarakham Thailand Faculty of Engineering at Mahasarakham University Research Unit for Computational Electromagnetics and Optical Systems (CEMOS) Mahasarakham Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This study explores techniques to mitigate interference in assessing functions related to AM radio wave signals in digital media receivers. Signal transmission cable testing focused on BNC RG59 75-ohm cables with length of 300 mm. Six methods were employed: Method 1 used a Ferrite core, Method 2 used copper tape, Method 3 connected a grounding wire, Method 4 combined grounding wire connection with a Ferrite core, Method 5 combined grounding wire connection with copper tape, and Method 6 combined grounding wire connection with both a Ferrite Core and copper tape. The result has been shown that Method 6 yielded optimal results, with AM 1000 kHz Usable Sens, AM Interstation Noise L, and AM Interstation Noise R functions differing from reference values by −3.53 dBm, −5.05 dBm, and −5.26 dBm, respectively, emphasizing its efficacy in enhancing the assessed functions.

关键词： Ferrites Grounding Wires Noise Receivers Interference Media

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Decision Tree-Based IoT Botnet Attack Detection

Decision Tree-Based IoT Botnet Attack Detection

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors.

关键词： Training Logistic regression Botnet Predictive models Bayes methods Internet of Things Decision trees

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