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Adaptive PV Systems: Using Genetic Algorithm and Random Forest Classifier to Mitigate the Effects of Partial Shading on Solar Photovoltaic Systems

Adaptive PV Systems: Using Genetic Algorithm and Random Fore...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Cabanayan, Ivan Perry M. Rodriguez, Adrian Enrico D. Peña, Robert Alfie S. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Lab Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350350821

Mismatch losses due to partial shading of solar photovoltaic (PV) arrays can limit their generated power. One method to improve their power output is to electrically reconfigure the array based on the principle of row irradiance equalization. Shaded panels in an array are identified to determine the array's optimal configuration and maximize its power output. In this study, a random forest classification model was developed to identify the partial shading pattern incident on an array. Features of the model included the individual panel currents and the array irradiance of preset 20 shading patterns. The data used to train the model was obtained using an off-grid 3× 3 array composed of nine 10-W PV modules in total-cross-tied (TCT) topology. The model achieved a mean accuracy of 90% using the testing data and 98% using 5-fold cross-validation. This was integrated with a genetic algorithm-based irradiance equalization heuristic, which takes a predicted shading pattern as input and outputs an optimized configuration. For each shading pattern, the suggested reconfiguration was recorded and its corresponding power change was calculated. Power recovery ranged from -15.70% to 40%. The 3× 3 PV array reconfigured successfully to the optimum configuration. © 2024 IEEE.

关键词： Photovoltaics

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Prototype Development of a Multilevel Solar Dryer for Agriculture

Prototype Development of a Multilevel Solar Dryer for Agricu...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Fernandez, Gian Carlo C. Luis, Jed Matthew A. San Peña, Robert Alfie S. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Lab Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350350821

Basic food processing in the Philippine agricultural sector relies on drying, which makes food products less perishable. A common practice is traditional sun-drying, which is simple, but exposes the produce to contaminants. This study developed a multilevel solar dryer for agriculture to provide better drying conditions while keeping costs down. A temperature and humidity monitoring system was built into the dryer to control its cooling fans and the humidity inside. The dryer demonstrated better drying conditions with an average drying temperature of 47.38 °C compared to the traditional sun-drying's 39.38 °C. In addition, the solar dryer's inside humidity was also better with a recorded average of 31.29% RH compared to the traditional setup's 39.66% RH. The dryer successfully dried a hybrid of Capsicum frutescens and Capsicum annuum or siling tingala, as it is locally known, at a drying rate faster than the traditional sun-drying setup. Overall, the multilevel solar dryer was able to provide better drying conditions, a faster drying rate, and a protected enclosed space for drying food products. © 2024 IEEE.

关键词： Solar dryers

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Proactive Congestion Management in Next-Generation Wireless Networks: Evaluating Traditional Approaches Against Machine Learning Models 2

Proactive Congestion Management in Next-Generation Wireless ...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Thangaraj, Johnjustin Mohanraj, T. Caleb, S. Srija, J. Latha, R. Rajendran, Pushpalatha Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Coimbatore Department of Computer Science Engineering India Karunya University Karunya Institute of Technology and Sciences Department of Computer Science and Engineering India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communications Engineering Chennai India

ISBN: (纸本)9798350364699

sensible congestion control approaches continue to be imperative in attaining the highest capability of the next-generation facilities for wireless networks. To this end, the objective of the present research is to establish the effectiveness of the study conducted with classical congestion management techniques to that of the machine models. The proposed work environments a novel and controlled setup of the next-generation wireless network and applies, assesses, and optimizes the traditional QoS and traffic-shaping methodologies, and generic AI forms of the supervised learning and reinforcement learning. Other standards that were utilized and incorporated in the efficient aspect included the latency, through put and packet loss with the view of evaluating efficiency levels of the employed methods. This simply measures that the use of ML is more superior to the conventional methods because it relieves latency and packet loss while at times enhancing through put. The analysis of the two states' comparison proof that congestion recovery problems are more fit for the application of ML algorithms in modern transport systems due to better flexibility and online decision-making chances. Nevertheless, it discusses some problems that exist and they include;The major challenge of how to integrate the ML model in the system and the need to obtain massive training data. Therefore, one can state that the approach of applying more preventive ML models in controlling congestion also appears to be wiser when it comes to deploying lesions over wireless networks from the viewpoints of efficiency and scalability. Future work includes: working with real data;further work on the Advanced ML section;Security and Compliance. © 2024 IEEE.

关键词： Efficiency

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A Deep Learning Model-Based Neural Network Framework for Diabetic Foot Ulcer Classification 5

A Deep Learning Model-Based Neural Network Framework for Dia...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shanmugam, P. Kumar, P.K. Manoj Hussain, Mohammad Manzoor Revathi, R. Deepa, P. Sakthivelu, U. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai India B v Raju Institute of Technology Department of Computer Science and Engineering Medak Telangana Narsapur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Texcity Arts and Science College Department of Computer Science Madukkarai Market Coimbatore India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350379945

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, frequently resulting in amputation and having a significant impact on patients' quality of life. Early and accurate DFU classification is essential for prompt and efficient therapy. This study introduces an innovative neural network framework that utilizes deep learning techniques to categorize DFU based on thermograms. The proposed framework employs the pre-trained EfficientNet-B0 deep learning model, well-known for its exceptional performance in image classification tasks. The framework underwent training and assessment using a dataset sourced from Kaggle, consisting of 1866 colored images. Among them, 890 images depict plantar thermograms with negative DFU, whereas 976 images depict foot thermograms with positive DFU. The proposed framework demonstrated a higher accuracy of 98.71, outperforming previous approaches by a wide margin. The findings demonstrate the ability of deep learning to categorize DFU, paving the way for the development of automated testing techniques to assist healthcare practitioners in the timely identification and treatment of this incapacitating ailment. © 2024 IEEE.

关键词： Ability testing

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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MayGAN:Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

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computer Systems Science & engineering 2023年第8期46卷 2039-2058页

作者： Neenavath Veeraiah Youseef Alotaibi Ahmad F.Subahi Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaA.P.521180India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

Leukemia,often called blood cancer,is a disease that primarily affects white blood cells(WBCs),which harms a person’s tissues and *** condition may be fatal when if it is not diagnosed and recognized at an early *** physical technique and lab procedures for Leukaemia identification are considered *** is crucial to use a quick and unexpected way to identify different forms of *** screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require *** deep-learning(DL)model-based segmentation and categorization techniques have already been introduced,although they still have certain *** order to enhance feature extraction and classification in such a practical way,Mayfly optimization with Generative Adversarial Network(MayGAN)is introduced in this ***,Generative Adversarial System(GAS)is integrated with Principal Component Analysis(PCA)in the feature-extracted model to classify the type of blood cancer in the *** semantic technique and morphological procedures using geometric features are used to segment the cells that makeup *** lymphocytic Leukaemia(ALL),acute myelogenous Leukaemia(AML),chronic lymphocytic Leukaemia(CLL),chronic myelogenous Leukaemia(CML),and aberrant White Blood Cancers(WBCs)are all successfully classified by the proposed MayGAN *** proposed MayGAN identifies the abnormal activity in the WBC,considering the geometric *** with the state-of-the-art methods,the proposed MayGAN achieves 99.8%accuracy,98.5%precision,99.7%recall,97.4%F1-score,and 98.5%Dice similarity coefficient(DSC).

关键词： Leukemia blood smear images optimization classification neural networks

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Optimal Detection in MIMO Systems Using Spatial Sigma-Delta ADCs 58

Optimal Detection in MIMO Systems Using Spatial Sigma-Delta ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Nguyen, Toan-Van Nassirpour, Sajjad Atzeni, Italo Tolli, Antti Swindlehurst, A. Lee Nguyen, Duy H. N. San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States University of Oulu Centre for Wireless Communications Finland University of California Department of Electrical Engineering and Computer Science IrvineCA United States

ISBN: (纸本)9798350354058

The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed. © 2024 IEEE.

关键词： Mean square error

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Fuzzy Inference System for Predicting Type of Delivery: A Valuable Smart Tool for Obstetrics and Gynecology 8

Fuzzy Inference System for Predicting Type of Delivery: A Va...

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8th International Conference on Smart and Sustainable Technologies, SpliTech 2023

作者： Mansour, Ayman M. Tareq Amman Jordan Tafila Technical University College of Engineering Department of Computer and Communications Engineering Tafila6611 Jordan

ISBN: (纸本)9789532901283

In obstetrics and gynecology, a fuzzy inference system has emerged as a valuable tool for predicting delivery type in advance by considering various factors, such as the mother's age, baby head measurement, pelvis size, body mass index, blood pressure, general health risk, and gestational diabetes. The system calculates a risk score based on these factors, aiding healthcare providers in making informed decisions about the delivery process and improving outcomes for both mother and baby. By utilizing membership functions and defuzzification techniques, the system ensures that predictions are based on a comprehensive analysis of all relevant factors. In conclusion, this fuzzy inference system is an essential tool for enhancing the quality of maternal and neonatal care, offering recommendations to expecting families while providing physicians with an advisory system. Developed using MATLAB, the system was tested with real data and exhibited high accuracy of 95%, with physician validation confirming a perfect match with their decision-making. © 2023 University of Split, FESB.

关键词： Forecasting

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Trajectory-Driven Efficiency: An Energy-Efficient Opportunistic Routing Protocol for Industrial IoT Wireless Sensor Networks 10

Trajectory-Driven Efficiency: An Energy-Efficient Opportunis...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajulu, G. Govinda Murugesan, M. Mahaboog Basha, S.K. Kalaiselvan, S.A. Kumar, M. Lenin Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India

ISBN: (纸本)9798350353068

The rapid expansion of Wireless Sensor Networks (WSNs) in the realm of Industrial Internet of Things (IIoT) demands sophisticated routing protocols to ensure energy efficiency and reliable communication. This paper introduces a novel Energy-Efficient Opportunistic Routing Protocol Based on Trajectory Prediction (EORB-TP) to address these challenges in industrial environments. The research identifies the need for dynamic adaptation to industrial dynamics and changing environmental conditions, prompting the development of trajectory prediction models using Hidden Markov Models (HMMs). EORB-TP intelligently leverages these models for dynamic routing decisions, strategically optimizing data transmission paths to minimize energy consumption. The proposed system's efficiency is demonstrated through a comprehensive experimental study, showcasing superior performance in terms of network lifetime, energy consumption, end-to-end delay, and routing cost compared to existing protocols. This innovative protocol offers a promising solution for enhancing the sustainability and efficiency of Wireless Sensor Networks in Industrial IoT applications, paving the way for resilient and resource-conscious industrial monitoring systems. © 2024 IEEE.

关键词： Routing protocols

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Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

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computer Systems Science & engineering 2023年第8期46卷 1879-1900页

作者： Neenavath Veeraiah Youseef Alotaibi Ahmad FSubahi Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaA.P.521180India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

An abnormality that develops in white blood cells is called *** diagnosis of leukemia is made possible by microscopic investigation of the smear in the *** training is necessary to complete the morphological examination of the blood smear for leukemia *** paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support *** proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear *** operations are used to crop the nucleus based on automated *** Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer *** entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the *** proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation *** in WBC imaging characteristics depends on the cytoplasmic and nuclear *** computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear *** classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression *** designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the *** proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape *** exhibits a higher classification accuracy value of 99.6%when combined with the other *** comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.

关键词： White blood cells leukemia segmentation histogram blood smear

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