This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller(AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory ofcontrol (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposedSC algorithm involve the features of finite-time convergence, unaffected by parameter variations, andchattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controllermore robust and is utilized to estimate the undefined converter nonlinear dynamics without resortingto the usual linearization and simplifications of the converter model. Taking a switching DC-DC buckconverter as a demonstration, the suggested AF-FTSC is thoroughly analyzed and executed on adSPACE ds1103 controller board. The outcomes of the experiment confirm the competence andapplicability of the suggested regulator.Keywords: Synergetic control, Fuzzy logic system, Fast terminal method, Finite-time convergence, DC/DCbuck converter

Hate speech has been disseminated more frequently on social media sites like Facebook in recent years. OnFacebook, hate speech can proliferate through text, image, or video. We suggested a deep learning approach toidentify offensive memes posted on Facebook in case of Amharic language’. The research process commenced bymanually gathering memes posted by Facebook users. Next came textual data extraction, annotation, pre-processing, splitting, feature extraction, model development and assessment Amharic OCRs were employed toextract textual data. Character normalization, stop word removal, and unnecessary character removal make upthe text-preprocessing step. Using Stratified KFold the textual dataset is split into the train set (80 %), thevalidation set (10 %) and the test set (10 %). Vectors are created from the preprocessed texts using the Bog ofwords (BOW), TFIDF and word embeddings. Following that, the vectors are fed into Machine learning algo-rithms: NB, DT, RF, KNN, LSVM and LR, and deep learning models that are based on Dense, BiGRU, and BiLSTMalgorithms. The model with the optimal parameters is chosen after numerous experiments. With an accuracy rateof 94 %, the BiLSTM + Dense model, the suggested technique identified nasty meme posts on Facebook written inAmharic.
Keywords: Deep learning, BILSTM, BIGRU, Amharic language hate speech

Object tracking is a technique for finding moving objects of interest and estimating their trajectoryor path with regard to time in a series of images. It involves object representation, detection,and tracking. It becomes an important field of study due to the need in video surveillance, trafficmonitoring, live sport video analysis and many other applications. In this paper, both static camera-based and dynamic camera-based object tracking techniques have been developed. The static camera-based object tracking was developed with NI LabVIEW, and Shape adaptive mean-shift algorithmhas been used for tracking. In case of dynamic camera-based object tracking, an optimal Fuzzy-PIDcontroller has been designed to adjust the position of the pan/tilt mechanism so as to trace the object’strajectory. Genetic algorithm (GA) was used to find the optimal values of the operating ranges (scalingfactors) of the membership functions. The performance of the system has been tested by differenttrajectories like step, sinusoidal, circular and elliptical at different frequencies 1, 50 and 100 rad/sec.The system has best performance at low frequencies and when the frequency or speed of the objectincreases, the system performance decreases which complies for real systems. The simulation resultsdemonstrate that GA tuned Fuzzy-PID controller has given us the best results in terms of reducedsteady-state error, faster rise time and settling time, and object position stabilization than PID,Fuzzy and Fuzzy-PID controllers, which shows that optimal Fuzzy-PID controller designed is moreappropriate and efficient.Keywords: Object tracking, LabVIEW, Fuzzy-PID, Pan/Tilt system, Genetic algorithm

This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizingthe placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks(CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltagestability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects.The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms establishedmethods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale OptimizationAlgorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stablevoltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach toenhancing both technical performance and operational reliability in distribution systems. Additionally,the consideration of temperature effects on PVRES efficiency adds depth to the study, making it avaluable contribution to the field of power system optimization.Keywords: Efficient Metaheuristic BitTorrent (EM-BT) algorithm, Photovoltaic renewable energy sources(PVRES), Capacitor banks (CBs), Energy loss minimization, Particle Swarm Optimization (PSO), Grey WolfOptimizer (GWO), Whale Optimization Algorithm (WOA), Operational reliability

This study investigates the performance of hemispherical solar stills (HSS) enhanced with date kernelsand olive kernels as heat storage materials to improve water distillation efficiency. By utilizing thesenatural and sustainable materials, the research highlights an alternative to synthetic options. Rigorousexperimentation and detailed analysis under identical conditions reveal that both kernels significantlyimprove heat retention and water production rates. The HSS with date kernels (HSSDK) achieved adaily water productivity of 6.66 kg/m2 day, representing an efficiency increase of 10.87%, while theHSS with olive kernels (HSSOK) produced 8.00 kg/m2 day, enhancing efficiency by 13.54%. The cost perm3 of distilled water for HSSDK is approximately USD 4.65, while HSSOK costs USD 3.89, comparedto USD 7.83 for the conventional CHSS system. These results demonstrate that the inclusion of heatstorage materials has significantly reduced the cost of water production, with reductions of about 40%for HSSDK and 50% for HSSOK compared to the conventional system. These results are attributedto the high thermal conductivity and specific heat capacities of the kernels, enabling effective heatstorage and gradual release. This study demonstrates the potential of agricultural by-products ascost-effective and sustainable solutions for solar water distillation. Further research is recommendedto optimize the quantities and configurations of these materials, as well as to explore their integrationwith other renewable energy systems to enhance overall efficiency and sustainability.Keywords: Hemispherical solar still, Date kernels, Olive kernels, Heat storage materials, Distillation efficiency

This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR)prediction models. The prediction is ensured for a period ranging from a few hours to several days ofthe year. These models are derived from four machine learning methods, namely the Feed-forwardBack Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method,Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combinesConvolutional Neural Networks and Long Short-Term Memory networks. This combination results inthe CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root MeanSquared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), andNormalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by eachmodel above and the actual output, pre-recorded in the experimental trial. The experimental resultsconsistently show the power of the CNN-LSTM model compared to the remaining models in terms ofaccuracy and reliability. This is due to its lower error rate and higher detection coefficient (R2 = 0.99925).Keywords: Artificial neural networks, Convolutional neural network, Convolutional feed-forward backpropagation, Deep learning, Feed-forward back propagation, Long short-term memory, Solar radianceforecasting

In engineering applications, the bearing faults diagnosis is essential for maintaining reliability andextending the lifespan of rotating machinery, thereby preventing unexpected industrial productiondowntime. Prompt fault diagnosis using vibration signals is vital to ensure seamless operation ofindustrial system avert catastrophic breakdowns, reduce maintenance costs, and ensure continuousproductivity. As industries evolve and machines operate under diverse conditions, traditional faultdetection methods often fall short. In spite of significant research in recent years, there remains apressing need for improve existing methods of fault diagnosis. To fill this research gap, this researchwork aims to propose an efficient and robust system for diagnosing bearing faults, using deep andShallow features. Through the evaluated experiments, our proposed model Multi-Block Histogramsof Local Phase Quantization (MBH-LPQ) showed excellent performance in classification accuracy, andthe audio-trained VGGish model showed the best performance in all tasks. Contributions of this workinclude: Combine the proposed Shallow descriptor, derived from a novel hand-crafted discriminativefeatures MBH-LPQ, with deep features obtained from VGGish pre-trained of Convolutional NeuralNetwork (CNN) using audio spectrograms, by merging at the score level using Weighted Sum (WS).This approach is designed to take advantage of the complementary strengths of both feature models,thus enhancing overall bearing fault diagnostic performance. Furthermore, experiments conductedto verify the approach’s performance is assessed based on fault classification accuracy demonstrateda significant accuracy rate on two different noisy datasets, with an accuracy rate of 98.95% and 100%being reached on the CWRU and PU datasets benchmark, respectively.Keywords: Bearing fault diagnosis, Vibration signals, Transfer learning, Shallow descriptor, Deep features,MBH-LPQ, VGGish, CNN

This paper addresses the smart management and control of an independent hybrid system based onrenewable energies. The suggested system comprises a photovoltaic system (PVS), a wind energyconversion system (WECS), a battery storage system (BSS), and electronic power devices that arecontrolled to enhance the efficiency of the generated energy. Regarding the load side, the systemcomprises AC loads, DC loads, and a water pump. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT technique is suggested to enhance the efficiency of the PVS and WECS. This technologyprovided good performance compared with the Perturb and Observe (P&O) algorithm and MPPT-basedfuzzy logic controller (FLC). The use of the ANFIS-PI proposed to control the bidirectional converteraccomplished voltage stabilization for the DC bus. This work also came with a fuzzy logic-basedalgorithm to manage the load side that depends on battery charge ratio, solar radiation, and windspeed. According to results obtained in the MATLAB/Simulink environment, the proposed technologieswere found to have performed well. The goal we were also pursuing was achieved through the fulluse of the energy generated by the proposed algorithm. The proposed study holds great potential forremote regions.Index terms: Renewable energy, Hybrid system, MPPT, ANFIS controller, Management

While Model Predictive Control (MPC) has been widely studied in power electronics, its real-time imple-mentation on DC-DC boost converters—particularly under variable loading conditions—remains limited. Thispaper proposes a new real-time implementation of the Model Predictive Control (MPC) law for a DC-DC boostconverter connected to variable loads. This implementation ensures precise current regulation through accurateduty cycle control updates, enabling the inverter’s frequency switching to be activated or deactivated as needed.This is achieved by proposing a predictive model of the current occurring in the first channel of the convertermodel, where a fitness function—comprising reference tracking and control effort—is minimized. Compared tothe proportional-integral (PI) controller, the MPC law proves more efficient, particularly in preventing oscilla-tions in both transient and steady-state output current responses. This advantage is validated through experi-mental tests for either a current inductance load or a resistive load. Since this type of real-time implementationhas not been previously applied on this converter, it constitutes the main contribution of this paper.
Keywords: PI controller, DC-DC boost converters, Model predictive control (MPC), Experimental validation

This paper presents a novel approach to modeling and controlling a solar photovoltaic conversionsystem(SPCS) that operates under real-time weather conditions. The primary contribution is theintroduction of an uncertain model, which has not been published before, simulating the SPCS’sactual functioning. The proposed robust control strategy involves two stages: first, modifying thestandard Perturb and Observe (P&O) algorithm to generate an optimal reference voltage usingreal-time measurements of temperature, solar irradiance, and wind speed. This modification leadsto determining and linearizing the nonlinear current-voltage (I-V) characteristics of the photovoltaic(PV) array near standard test conditions (STC), resulting in an uncertain equivalent resistance used tosynthesize an overall model. In the second stage, a robust fixed-order H∞ controller is designed basedon this uncertain model, with frequency-domain specifications framed as a weighted-mixed sensitivityproblem. The optimal solution provides the controller parameters, ensuring good reference trackingdynamics, noise suppression, and attenuation of model uncertainties. Performance assessments atSTC compare the standard and robust P&O-MPPT strategies, demonstrating the proposed method’ssuperiority in performance and robustness, especially under sudden meteorological changes andvarying loads. Experiment results confirm the new control strategy’s effectiveness over the standardapproach.