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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Alif Aditya Wicaksono I Ketut Eddy Purnama Reza Fuad Rachmadi Feby Artwodini Muqtadiroh Ardyono Priyadi Mauridhi Hery Purnomo Dept. of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

In this paper we purpose several methods for creating 2D projection image from a 3D scan such as from CT or MR scans, and then look at different types of 2.5D projections with a growing window sizes and see its effects on the efficacy of the machine learning model when combined with the selection algorithms. As such we are using Shi-Tomasi Detectors, Sobel Edge Detectors, Sum of Pixel Intensity, Center Select, and Random Selection as selection algorithms from which we select the most interesting index for a given scan, from which depending on the window size, we stack the neighboring slices in the axial plane to the channel dimension creating a 2.5D image from which we could pass into our ResNet-9 model configured for classification and train the model for a given number of epochs, under the same conditions for all of our tests, this ensures that for all of our experiments the main effector is window size and the selection algorithm. From our tests we found that the Center Select on average outperforming the other selection algorithm, this result indicate that medical information is mostly centered around the scan, and when combined with the saving of 60% less parameters, the purposed method shows a new way of processing 3D Scans for processing with a 2D CNN model.

关键词： Solid modeling Three-dimensional displays Machine learning algorithms Image edge detection Computed tomography Neural networks Detectors Machine learning Classification algorithms Indexes

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Stock Price Prediction Using LSTMSeq2Seq: A Deep Learning Approach with Advanced Feature engineering

Stock Price Prediction Using LSTMSeq2Seq: A Deep Learning Ap...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Kalim Amzad Chy Abdul Kadar Muhammad Masum Mohammed Shamsul Alam Md. Abul Kalam Azad Md Zia Uddin Dept. of AI Innovation Liberate Labs Texas USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Business and Technology Management Islamic University of Technology Gazipur Bangladesh Software and Service Innovation Department SINTEF Digital Oslo Norway

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper explores stock price prediction in the context of the Bangladesh stock market using advanced deep learning methodologies, specifically the LSTMSeq2Seq model. Traditional analytical techniques often fail to capture the complex, non-linear patterns inherent in financial data. To address this limitation, our research leverages the strengths of the LSTM-Seq2Seq architecture, which is tailored to the unique dynamics of the Bangladesh market. A robust feature engineering process is employed, utilizing 67 diverse features, including market trends, stock-specific indicators, fundamental and technical variables, and key macroeconomic factors such as gold prices and foreign exchange reserves. The dataset encompasses historical stock market data from 2012 to 2022, and extensive data preprocessing is performed to handle inconsistencies. Hyperparameter optimization is conducted using grid search to ensure model performance. The LSTMSeq2Seq model is evaluated based on key metrics such as MAE, MAPE, MSE, and the R 2 score, demonstrating superior predictive capabilities compared to traditional methods. This research not only advances the application of deep learning in financial forecasting but also provides valuable insights into stock price prediction in emerging markets, addressing ethical concerns to mitigate risks like market manipulation.

关键词： Deep learning Measurement Sentiment analysis Computational modeling Time series analysis Predictive models Macroeconomics Stock markets Forecasting Long short term memory

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MTDC Fault Detection and Localization Using High-Pass Filter

MTDC Fault Detection and Localization Using High-Pass Filter

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Annual Conference of Industrial Electronics Society

作者： Saad Ahmed Khan Abhisek Ukil Nirmal-Kumar C. Nair Dongyu Li Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Electrical and Automation Engineering East China Jiaotong University Nanchang China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The DC link capacitor discharge in the event of a DC fault is rapid and contains high frequencies. This rapid discharge of high current and frequency interface with DC bus, Voltage Source Converter (VSC), and AC source. This interface damages the equipment and possibly living beings in proximity. Therefore, it is necessary to develop a topology to detect and identify fault types promptly, along with the isolation and restoration of the system. This study introduces an improved novel fault detection technique using the Highpass Chebyshev type 2 filter due to its flatter pass-band response. Further, the polarities of peaks obtained from the fault detection calculation are used again for another proposed novel fault location method. It identifies fault types to locate and isolate the faulty system using only polarities of amplitude response peaks. In the simulation, both methods were fast and accurate in detecting, locating, and identifying appropriate fault types only by High Pass Filter (HPF) amplitude response peaks and their polarities.

关键词： Accuracy Fault detection Voltage Fault location Chebyshev approximation Discharges (electric) Topology Power systems Passband Voltage-source converters

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Analysis of Perturb and Observe MPPT Under Varying PV Operating Climatic Conditions

Analysis of Perturb and Observe MPPT Under Varying PV Operat...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Abdirahim Addawe Musaria K. Mahmood Sulaiman M. Karim Ahmad M. Nezam dept. of Energy Systems Engineering Ankara Yildirim Beyazit University Ankara Turkiye dept. of Medical Instr. Tech. Eng. Jannat Iraq College Ramadi Iraq dept. of Computer Technology Engineering Al-Hadba'a University College Mosul Iraq dept. of Computer Engineering Karabuk Üniversitesi Karabuk Turkey

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Improving the efficiency of photovoltaic (PV) sys-tems has emerged as a critical priority. Achieving optimal power production from solar modules requires operating at maximum power point (MPP) under various climatic conditions. This paper describes the operation of the Perturb and Observe (P&O) as an efficient Maximum Power-Point Tracking (MPPT) algorithm. Simulations of various operating scenarios are performed using MATLAB to assess the algorithm efficiency under both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). This study considers factors such as output power efficiency, time response, and steady-state power oscillation. The simulation results show a fast-tracking, lower oscillation in the case of STC but fail to track the MPP in the case of multi-panel under PSC.

关键词： Photovoltaic systems Solar irradiance Simulation Production software Steady-state Time factors

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Web Application-Based Enhanced Esophageal Disease Diagnosis in Low-Resource Settings

Web Application-Based Enhanced Esophageal Disease Diagnosis ...

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IEEE International Conference on Biomedical engineering, computer and Information Technology for Health

作者： Abdullah Al Masum Zishad Hossain Limon Md Ariful Islam Md Shahidur Rahman Mahbub Khan Sheikh Shemanto Afridi Md. Redwan Ahmed Shafiur Rahman Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Engineering management Westcliff University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

An essential part of the digestive system, the esophagus permits food and liquids to move easily from the throat to the stomach. Traditional diagnostic techniques, such as endoscopy and biopsy, are invasive and resource-intensive, making them less practical in resource-constrained environments. To address these challenges, we proposed utilizing a hybrid Deep Learning (DL) model called GAN-EfficientNet, which combines the power of Generative Adversarial Networks (GAN) and EfficientNet to effectively classify esophageal diseases. Our approach employs advanced image preprocessing techniques and data augmentation methods to enhance the diversity and balance of the dataset. Feature extraction using the Spatial Gray Level Dependence Method (SGLDM) and Principal Component Analysis (PCA) significantly reduces computational complexity without compromising accuracy. We explored both binary and multiclass classification methods, recognizing their crucial roles in various diagnostic scenarios. Binary classification distinguishes between normal and diseased states, essential for initial screening, while multiclass classification identifies specific esophageal diseases, aiding in personalized treatment plans. The GAN-EfficientNet model demonstrated exceptional performance, achieving an impressive F1 score of 99.44% for binary classification and 99.64% for multiclass classification. Additionally, we developed a web application that facilitates quick and precise disease diagnosis, particularly advantageous in areas with limited resources. This tool significantly improves patient outcomes through early detection, leading to reduced healthcare costs and less burden from advanced esophageal diseases.

关键词： Deep learning Accuracy Computational modeling Generative adversarial networks Feature extraction Data augmentation Data models Medical diagnosis Diseases Principal component analysis

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks

A Comparative Study of the Performance of Real time database...

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International Multi-Topic ICT Conference (IMTIC)

作者： Syed Muhammd Nabeel Mustafa Muhammad Umer Farooq Syeda Sundus Zehra Jospeh Peter Terenece Noronha Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics.

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Skin Color-Based Frontal Face Detection and Gender Classification

Skin Color-Based Frontal Face Detection and Gender Classific...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Daghan Dogan Tankut Acarman Mechanical Systems and PCB Design Tubitak Bilgem Kocaeli Turkiye Computer Engineering Dept. Galatasaray University Istanbul Turkiye

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Face detection is necessary for many different applications: face recognition, image database management, etc. In this study, a face detection algorithm is performed on images based on human skin color, and the detected face image is classified based on gender. In this algorithm, three color spaces (RGB, HSV, YCbCr) are used and face detection is performed by combining different thresholds in these three spaces. Outputs of the algorithm are used as feature vectors for the gender classification. The genders in the images are classified with 100% accuracy using the different classification methods.

关键词： Accuracy Image color analysis Face recognition Nose Mouth Machine learning Skin Vectors Classification algorithms Face detection

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SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in Satellite IoT

SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Rodrigo Munoz-Lara Sandra Céspedes Marcos Diaz Dept.of Electrical Engineering University of Chile Santiago Chile Dept. of Computer Science & Software Eng. Concordia University Montreal QC Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Static Context Header Compression and Fragmentation (SCHC) is a standard defined as an adaptation layer for supporting IPv6, UDP, and CoAP protocols in low power wide area network (LPWAN) technologies. SCHC has fragmentation modes that use confirmation mechanisms to deliver reliable communications. Direct-to-satellite Internet of Things (DtS-IoT) directly communicates between end devices and the satellite in an IoT environment. If SCHC is implemented in a DtSIoT environment, applications using IPv6 could have global coverage through the Internet of Things (IoT). Unfortunately, it is not known whether SCHC can operate with the current acknowledgement mechanisms in a DtS-IoT environment, and if so, it is not known which one performs better. This work evaluates SCHC acknowledgement modes for ACK-on-Error fragmentation mode, including the new SCHC Compound ACK mode. It determines which mode has the lowest transfer delay for different LoRa data rates and frame error rates in a DtS-IoT environment with LEO satellites.

关键词： Satellites Protocols Low earth orbit satellites Delays Internet of Things Compounds Reliability IP networks Standards Low-power wide area networks

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Machine Learning Approaches To Predict The Stability of Smart Grid

Research Square

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Research Square 2024年

作者： Satu, Shahriare Khan, Imran Dept. of Management Information Systems Noakhali Science and Technology University Noakhali3814 Bangladesh Dept. of Computer Science and Engineering Gono Bishwabidyalay Dhaka1344 Bangladesh

The smart grid is a modern solution to generate, distribute, and use energy effectively and efficiently. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to identify smart grid stability more efficiently. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the correlation of individual features. Different feature selection and transformation techniques were employed to generate subsequent feature sets. Then, various classification techniques were applied to the primary and its subsets to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP showed the highest accuracy (97.06%) for the Z-score transformed data set using Grid Search Optimization. This study demonstrates the potential of using machine learning to predict smart grid stability accurately and efficiently, providing a critical tool for improving the reliability, efficiency, and sustainability of the energy industry. © 2024, CC BY.

关键词： Smart power grids

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Inflation Prediction: A Comparative Study of ARIMA and LSTM Models Across Different Temporal Resolutions

Inflation Prediction: A Comparative Study of ARIMA and LSTM ...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： T Lakshmi Narayanaa R R Skandarsini S. Jhansi Ida S. Rathana Sabapathy P Nanthitha Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India Dept. of Information Technology Panimalar Engineering College Chennai India

This research study focuses on the prediction of inflation rates using two distinct models, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM), tailored to accommodate the temporal resolutions of monthly and quarterly inflation data. While a comprehensive evaluation across varying time horizons was not conducted, the results suggest that the ARIMA model excels in short-term forecasting, as evidenced by its impressive performance on a 2021 holdout dataset. Conversely, the LSTM model displays potential for medium-term predictions. This study underscores the significance of aligning forecasting models with data characteristics, emphasizes the importance of temporal resolution, and discusses potential avenues for improvement, such as multivariate modeling or alternative techniques like Prophet. The findings have broad implications for economists, policymakers, and analysts seeking precise inflation forecasts, shedding light on the nuanced interplay between models and data in the domain of inflation prediction.

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