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Digital Society and Intelligent systems (DSInS), International Conference on

作者： Wahidur Rahman Md Shadman Sakib Md Solaiman Hosen Mir Mohammad Azad Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan Asadullah Shaikh Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh Department of Computer Science Iowa State University USA Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science College of Computer Science and Information Systems Najran University Saudi Arabia Emerging Technologies Research Lab (ETRL) College of Computer Science and Information Systems Najran University Saudi Arabia Department of Information Systems College of Computer Science and Information Systems Najran University Saudi Arabia

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Because of poor blood flow and susceptibility to infections, breast cancer can be challenging to heal and, if not treated, might cause major problems, including limb amputation and a lowered quality of life. Though several systems exist to identify breast cancer, few combine machine learning (ML), deep learning (DL), and optimization strategies. This work presents a novel method leveraging complex algorithms to precisely identify breast cancer from ultrasound images. The study utilizes a dataset from a secondary source, categorizing it into three identical classes: normal (Class 0), benign (Class 1), and malignant (Class 2). It extracts features using pre-trained convolutional neural networks (CNNs), optimizes these features using the Differential Evolution Algorithm (DEA), and classifies images using standard machine learning algorithms. To show how well it detects breast cancer, the approach combines seven conventional ML classifiers with ten pre-trained CNN models. Using DenseNet121 + DEA + Xtreme Gradient Boosting (XGB) Classifier, DEA chooses important features derived by CNNs with a high accuracy of 97.76%. However, the proposed method can be very effective for real-time breast cancer screening using ultrasound images.

关键词： Limbs Ultrasonic imaging Machine learning algorithms Feature extraction Breast cancer Real-time systems Classification algorithms Convolutional neural networks Standards Optimization

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Assessing Bacteria Level with Machine Learning (ML) Models in the Neches River, Texas

Assessing Bacteria Level with Machine Learning (ML) Models i...

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World Environmental and Water Resources Congress 2025

作者： Mengjie He Qin Qian Xinyu Liu Jing Zhang Dept. of Industry and Systems Engineering Lamar Univ. Beaumont TX United States Dept. of Civil and Environmental Engineering Lamar Univ. Beaumont TX United States Dept. of Computer Science Lamar Univ. Beaumont TX United States

ISBN: (数字)9780784486184

ISBN: (纸本)9780784486184

Bacteria released from the on-site sewer facilities (OSSFs) elevated fecal bacteria indicators in the Neches River Tidal with increasing flood frequency and intensity. It is necessary to monitor the bacteria levels on time as elevated fecal bacteria bring a public health concern. The aim of the study is to collect water samples after rain events at hotspots of OSSFs to assess the bacteria impacts, apply innovative lab technology to track fecal bacteria sources from the selected water samples, and develop the classification ML model utilizing random forest (RF) for estimating bacteria levels in the Neches River Tidal to assist water monitoring. The field and lab measurements of the water sample showed higher Escherichia coli at hotspots of OSSF increased the bacteria level in the river, especially in winter. The RF determined bacteria levels in river depending on pH, temperature, dissolved oxygen (DO), alkalinity, phosphorus, sulfate, conductivity, chloride, total suspended solid (TSS), total nitrogen (TN), Secchi-dept., ammonia-N, and flow rate from five monitoring stations. RF achieved accuracy=0.74, precision=0.79, recall=0.86, and F1-score=0.83 with less overfitting, superior to the Decision Tree (DT) model. Flow rate was identified as the most important feature. The probability of elevated bacterial level significantly increased when the flow rate was greater than 250 m3/s although pH, temperature, and DO were also key biochemical factors. In conclusion, fixing failed OSSFs or applying green infrastructures are recommend approaches to eliminate the nonpoint source and decrease the bacteria level in Neches River Tidal.

关键词： bacteria level Best Management Practices (BMPs) machine learning (ML) nonpoint sources On-Site Sewer Facilities (OSSFs) Random Forest

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Analysis of the mechanism of degradation of p-type silicon solar cell

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Transactions of the Korean Institute of Electrical Engineers 2021年第8期70卷 1181-1187页

作者： Cha, Yewon Jeong, Sungjin Kim, Sungheon Kim, Youngkuk Yi, Junsin Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

Two main factors that limiting the efficiency of the p-type solar cells are LID(Light Induced Degradation) and LeTID(Light and elevated temperature induced degradation). The major cause of LID is the formation of boron-oxygen defects in boron doped silicon. The LeTID is similar kinds as LID, but also occurs in the dark condition and by the recombination of metals and impurities or the reaction between the hydrogen and arbitrary substance. An overall 7% of LeTID degradation rate occurs over long-term scales. Unexpectedly, these degradations have been recently found to also arises in high purity materials such as float-zone(Fz-Si) and Czochralski-grown(Cz-Si). The proposed method to reduce these two degradations is to use appropriate firing process and annealing conditions. © 2021 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Degradation

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AI-Driven Forecasting Mechanism for Cardiovascular Diseases: A Hybrid Approach using MLP and K-NN Models 2

AI-Driven Forecasting Mechanism for Cardiovascular Diseases:...

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2nd International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Chaganti, Koushik Reddy Krishnamurty, Pokuri Venkataradha Kumar, Addala Hemantha Gowd, G. Sudha Balakrishna, Chinnala Naresh, P. University of the Cumberlands Dept. of Computer and Information Systems KY United States Audisankara College of Engineering and Technology Cse Department Andhra Pradesh Gudur India Srinivasa Ramanujan Institute Of Technology Cse Department Anantapuram India Cse Department Hyderabad India Dayananda Sagar University Dept of Cse Bangalore India

ISBN: (纸本)9798350368413

Cardiovascular disease (CVD) results in malfunctioning of the heart and also the related blood vessels and regularly primes to death or corporal paralysis. Initial and automated discovery of CVD is crucial for saving many of the common humanoid lives. While several Investigations have been carried out to achieve this goal, and there is still room for development in terms of performance and reliability. This new work provides one more step in this area, since it utilizes two reliable ML algorithms, 'multi-layer perceptron (MLP)' and 'K-nearest neighbour (K-NN)'to detect CVD using publicly available data from the University of engineering California Irvine source. The models' concerts are enhanced optimally and by eradicating the possible outliers and common existing attributes with values that are treated as null. They performed general Experimental and effective findings show that the MLP model outperforms the KNN model in terms of reporting accuracy (82.47%) and area below the curve (86.41%). As a result, the proposed MLP model is recommended for automated CVD identification. Furthermore, this approach may be used to identify new illnesses, and the suggested model's routine can be tested against other standard datasets. © 2024 IEEE.

关键词： Nearest neighbor search

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Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment

Ensemble Learning for Seated People Counting using WiFi Sign...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Bernaola, Jose Ramon Merino Sobron, Iker Del Ser, Javier Landa, Iratxe Eizmendi, Inaki Velez, Manuel Dept. of Communications Engineering Bilbao48013 Spain Dept. of Languages and Computer Systems Bilbao Spain Derio48170 Spain

ISBN: (纸本)9781665423908

The detection, location, and behavior recognition of human beings in different environments is not only a subject of a wide range of studies, but has also triggered the development of a large number of applications, including those which enhance sustainability and efficiency of infrastructures. For instance, the estimation of the occupancy could improve the energy management of a building. Due to human presence or movement over a particular area, the analysis of variations in wireless signal properties of already deployed wireless technology such as WiFi systems provides the information needed for Machine Learning models to accomplish the non-intrusive (device-free) detection and classification of different human activities. In this context, this work focuses on detecting seated people in an indoor scenario by using ensemble learning, a particular branch of Machine Learning models for supervised learning that hinges on combining the outputs of individual predictors. Furthermore, we evaluate the transferability of the knowledge modeled by ensemble learners. When trained in a particular frequency or channel, such models are used to classify data captured over another different frequency. Our experimental setup and discussed results reveal that while ensembles attain satisfactory levels of predictive accuracy predictions, their knowledge cannot be transferred among different frequencies. This conclusion opens an exciting future towards new means to perform effective knowledge transfer over the frequency domain. © 2021 IEEE.

关键词： Learning systems

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

Exploiting Partial Observability and Optimized Simple State ...

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20th International Conference on Frontiers of Information Technology, FIT 2023

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

ISBN: (纸本)9798350395785

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-dept. 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. © 2023 IEEE.

关键词： Autonomous agents

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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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2023 International Conference on IT and Industrial Technologies, ICIT 2023

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

ISBN: (纸本)9798350315974

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. © 2023 IEEE.

关键词： Feature Selection

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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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2023 International Conference on IT and Industrial Technologies, ICIT 2023

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

ISBN: (纸本)9798350315974

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). © 2023 IEEE.

关键词： Feature Selection

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Quantum Wrapper Networking: Investigating the Noise Impact of Classical Headers on the Quantum Payload

Quantum Wrapper Networking: Investigating the Noise Impact o...

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2024 Frontiers in Optics, FiO 2024

作者： Gül, Gamze On, Mehmet Berkay Tan, Shannon G. Proietti, Roberto Kanter, Gregory S. Yoo, S.J. Ben Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy

We study the impact in time and frequency domains of classical headers on quantum payloads in quantum wrapper networking. We identify and characterize in-fiber scattering processes that produce noise photons degrading...

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Performance Improvement of Joint Space Distance Measurement for PIP joints

Performance Improvement of Joint Space Distance Measurement ...

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International Conference on Imaging, Signal Processing and Communication (ICISPC)

作者： Moeko Suzuki Tomio Goto Dept. of Creative Engineering Program Nagoya Institute of Technology Nagoya Japan Dept. of Computer Science Nagoya Institute of Technology Nagoya Japan

In order to analyze progresses of rheumatoid arthritis, we are developing an application to measure the distance between finger joints from X-ray images. In this paper, we focus on second joints, which are known to be prone to arthritis. We have proposed a method of filtering images and detecting edges, which has improved accuracy. Experimental results show that practical application of this system will reduce the burden on physicians and contribute to early detection of rheumatoid arthritis.

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