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Publication Experimental Verification of Rotating Sliding Mode Control with Composite Reaching Law Approach for Voltage Source Inverters(IEEE, 2025-02-18) Haroon, Faheem; Waqar, Asad; Aamir, Muhammad; Abbas, Syed Hayder; Ali, Syed Umaid; Mian Qaisar, Saeed; 0; Electrical and Computer Engineering; Haroon, Fahim; Energy Lab; 0; External Collaboration; 0In modern power systems, power electronic interfaces like Voltage Source Inverters (VSIs) uses advanced control strategies to ensure high-quality voltage regulation, significantly enhancing system stability and reliability for various applications. This paper introduces a novel voltage regulation method for VSIs using Sliding Mode Control (SMC) under extreme load variations [1]. Besides robustness, SMC is prone to chattering, causing power losses, reduced efficiency, and compromised transient response. To address chattering, an adaptive sliding surface selection mechanism that utilizes the Rotating Sliding Surface (RSS) technique and a new reaching law based on state variable magnitudes, which dynamically adjusts the control gain is proposed here. This composite reaching law uses exponential, power, and difference functions to achieve rapid convergence and reduce chattering. The sliding surface is chosen with a time-varying slope based on error variables. Tests on a single-phase VSI with varying loads demonstrate that the C-ERL-RSS SMC achieves a well-regulated output voltage with just 0.25% THD, reduced chattering, and minimal tracking time. Additionally, implementing the C-ERL-RSS SMC and PRERL SMC on a two-level three-phase VSI under varying load conditions shows superior efficiency and stability, with a low THD of 1.1%. Experimental evaluation on MicroLabBox-dSPACE 1202 for VSIs under extreme conditions reveals that the proposed technique excels in voltage regulation, offering fast transient response and low THD of 1.12% and 2.1% for single-phase and three-phase VSIs, respectively.Publication Spline Global MPPT with PID Controller Based on Levy Invasive Weed Technique for Renewable Energy Resources of Smart Homes(IEEE, 2024-09-23) Hussein, Aziza; ElKafrawy, Passent; Aly, Rabab; Mohamed, Mady; 0; Electrical and Computer Engineering; Hussein, Aziza; Energy Lab; 0; External Collaboration; 0Renewable energy systems, particularly photovoltaic (PV) systems, have been played important role in the reducing carbon emissions. A primary concern in the field of photovoltaics (PV) based on the design of the maximum power point tracking (MPPT) is the capacity to accurately monitor power across many parameters in addition to ascertain the power production of solar cells or wind turbines and adjust the load to maximise power efficiency under varying weather conditions. On another hands, the hybrid smart system uses a wind catcher to reduce the amount of energy consumed by buildings from the grid, which is a historically significant architectural component for cross ventilation and passive cooling. This paper presents an updating of model that proposes improvements to the regulation of Spline MPPT with tuning PID controller by using Levy Invasive Weed optimization (LIWO)technique. A rapid, accurate, and straightforward approach for determining the (MPPT) of PV systems under consistent irradiation and partial shade, as well as wind turbines, is presented using the Spline- MPPT technique. The model of this paper employs LIWO in conjunction with a PID controller to improve the selection of PID gains. Moreover, is applied to generate the optimal values of duty cycle of the model. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system. The model has attained an accuracy of 93%. The outcomes of this model contribute greatly to our understanding of the suitability and efficacy of both AI and traditional MPPT controllers. This modeling will be beneficial to the renewable energy industry.Publication Comparative Analysis of Deep Learning Architectures for Blood Cancer Classification(IEEE, 2024-12-25) Syed, Saad Ahmed; Nisar, Humaira; Yu Jen, Lee; Mian Qaisar, Saeed; Talpur, Shahnawaz; 0; Electrical and Computer Engineering; Syed, Saad Ahmed; Artificial Intelligence & Cyber Security Lab; 0; External Collaboration; 0An increased growth in the blood cancer necessitates the development of efficient, cost effective, timely, and accurate diagnosis. Traditional diagnosis methods are often invasive, expensive and time-consuming. The rapid artificial intelligence (AI) assistive advancement in the digital healthcare permits the realization of effective solutions in this framework. Specifically, the deep learning (DL), seems promising in an automated diagnosis. However, still a critical gap needs to be covered by understanding that which DL architecture performs better for the blood cancer detection. To address this crucial need, this paper presents a comprehensive comparative analysis of the key DL methods, used in an automated categorization of the blood cancer. The considered DL architectures are the MobileNetV2, DenseNet121, VGG16, ResNet50, and Inception V3.The applicability is tested using two blood cancer datasets namely the Acute Lymphoblastic Leukemia (ALL) dataset and American Society of Hematology (ASH) dataset. Each model is meticulously trained and evaluated on the ALL dataset for binary classification and the ASH image bank for multi-class classification. The categorization performance is evaluated based on accuracy, precision, recall, F1 score, and latency. Results have shown an out performance of the MobileNetV2 compared to the counter DL architectures with a mean accuracy of 91.26%, precision of 92.94%, recall of 91.27%, F1 score of 90.58% and latency of 104.16 mins for ALL dataset and 88.11 accuracy, 90.23% precision, recall of88.11 %, 87.98% F1 score and latency of 11.16 mins for ASH dataset.Publication Boosting Regression Assistive Predictive Maintenance of the Aircraft Engine with Random-Sampling Based Class Balancing(Springer, 2024-08-09) Barry, Ibrahima; Hafsi, Meriem; Mian Qaisar, Saeed; 0; Electrical and Computer Engineering; Barry, Ibrahima; Artificial Intelligence & Cyber Security Lab; 0; External Collaboration; 0This study presents the development of a data-driven predictive maintenance model in the context of industry 4.0. The solution is based on a novel hybridization of Remaining Useful Life (RUL) generation, Min-Max normalization, random-sampling based class balancing, and XGBoost regressor. The applicability is tested using the NASA’s C-MAPSS dataset, which contains aircraft engine simulation data. The objective is to develop an effective and Artificial Intelligence (AI) assistive automated aircraft engine’s RUL predictor. It can maximize the benefits of predictive maintenance. The rules based RUL generation provides a ground truth for evaluating the performance of intended regressors. The Min-Max normalization linearly transforms the intended dataset and scales the multi subject’s data in a common range. The imbalance presentation among intended classes can lead towards a biasness in findings. This issue is intelligently resolved using the uniformly distributed random sub-sampling. Onward, the performance of robust machine learning and ensemble learning algorithms is compared for predicting the RUL of the considered aircraft engine by processing the balanced dataset. The results have shown that the XGBoost regressor, uses an ensemble of decision trees, outperforms other considered models. The root mean square error (RMSE) and mean absolute error (MAE) indicators will be used to evaluate the prediction performances. The devised method secures the RMSE value of 12.88%. It confirms a similar or better performance compared to the state-of-the-art counterparts.Publication "Fiction to Function" Shaping Renewable Energy Education with MATLAB and ChatGPT-Driven Environments(IEEE, 2024-03-21) Kabbaj, Narjisse; Brahimi, Tayeb; Babiker, Safia; Aldybous, Alaa; Mohamed, Baher; 0; Electrical and Computer Engineering; Baher, Mohamed; Energy Lab; 0; Department Collaboration; 4Addressing the gap in effective and engaging renewable energy education, this study, 'Fiction to Function,' transitions imaginative frameworks into practical educational tools, focusing on solar and wind energy in line with Sustainable Development Goals (SDGs). The goal is to empower users with deep insights into renewable energy through the novel integration of MATLAB and ChatGPT-driven environments, enhancing the analysis and exploration of these energy sources. The study employs a structured approach using MATLAB App Designer to develop a user-friendly GUI, aligning instructional content with the SDGs and integrating ChatGPT via the OpenAI API for dynamic, interactive learning experiences. This integration provides a multifaceted platform that not only educates but also engages users in practical scenarios, bridging the gap between theoretical knowledge and real-world application. The results demonstrate an enhanced understanding of renewable energy concepts among users, showcasing the efficacy of combining MATLAB's analytical capabilities with ChatGPT's AI-driven guidance. Ultimately, the study highlights the transformative potential of innovative technology in sustainable energy education, setting a precedent for future educational tools in this domain.Publication Prediction of Adsorption and Desorption Isotherms for Atmospheric Water Harvesting(IEEE, 2024-03-21) F. El-Amin, Mohamed; 0; Master of Science in Energy Engineering; Kabbaj, Narjisse; Energy Lab; 0; Department Collaboration; 0This study improves the mathematical modeling for the potential of atmospheric water harvesting (AWH) using desiccant materials. This research is crucial in highlighting the sustainable water management strategies of AWH systems in converting atmospheric moisture into a vital water source. The primary focus of our research methodology was the analytical derivation of sorption isotherms, essential for the hygrothermal simulation of desiccant materials. This was accomplished through the application of two established models, namely, the Guggenheim-Anderson-de Boer (GAB) and Van Genuchten (VG). Experimental data on various anhydrous salts from existing literature have been used. An in-depth comparative analysis of these models reveals that the VG model aligns more closely with the experimental data, thus asserting its superiority in enhancing the selection and efficiency of desiccant materials in AWH systems. By confirming the VG model’s superiority in accurately modeling sorption isotherms, our research not only improves the model of AWH systems but also, importantly, contributes to the development of advanced water harvesting technologies.Publication Integrating Human-Centricity, Sustainability, and Resilience in Digital Twin Models for Industry 5.0 : A Multi-Objective Optimization Approach(2024) Bezoui, Madani; Slama, Ilhem; Bounceur, Ahcene; Mian Qaisar, Saeed; Hammoudeh, Mohammad; Turki Almaktoom, Abdulaziz; 0; Electrical and Computer Engineering; Bezoui, Madani; Artificial Intelligence & Cyber Security Lab; 0; University Collaboration; External Collaboration; 0This paper presents the InduDesc framework, an innovative digital twin model within the CupCarbon software, designed for the advanced needs of Industry 5.0. It integrates human-centred ergonomics, sustainability and resilience into the Flexible Job Shop Scheduling Problem (FJSP), traditionally an NP-hard challenge. By minimising operating times and balancing machine utilisation with ergonomic and sustainability considerations, the framework provides a dynamic workload management tool based on real-time 'fatigue' metrics. Using a tabu search algorithm, InduDesc generates a Pareto frontier to help decision makers identify strategies that efficiently align with the integrated goals of Industry 5.0.Publication Battery Management System for Enhancing the Performance and Safety of Lithium-Ion Batteries(IEEE, 2024-01-16) Mohammed, Abdul Majid,; Salma, Badaam; Arbaz, Ahmed; Haya, Binsalim; 1; Electrical and Computer Engineering; Haya, Binsalim; Energy Lab; 0; Department Collaboration; 1Battery packs integrated into the grid offer a promising solution for energy storage, but their efficient operation requires precise monitoring and control, which is achieved through Battery Management Systems (BMS). This paper proposes a temperature-dependent second-order RC equivalent circuit model to reflect the battery’s dynamic characteristics accurately. Then, a novel BMS design, incorporating Extended Kalman Filtering (EKF), a constant current-constant voltage (CCCV) charging, and a passive balancing algorithm to estimate the battery state of charge (SOC), balance voltage levels, and monitor thermal characteristics. The research also includes a comprehensive simulation study conducted in SIMULINK with the Simscape toolbox to assess the effectiveness of the analyzed BMS. The simulation results demonstrate the effictiveness of the proposed BMS design in monitoring the battery pack’s state, maintains cell balancing, estimates SOC, and keeps the temperature and current levels within safe limits. This model can help guide a more efficient and accurate BMS design for future studies.Publication Bayesian Optimization Algorithm for ConvLSTM-AE model To Forecast Solar Irradiation(IEEE, 2024-03-21) Boussetta, Mohammed; Bendali, Wadie; Morad, Youssef; Motahhir, Saad; Krichen, Moez; Mian Qaisar, Saeed; 0; Electrical and Computer Engineering; Boussetta, Mohammed; Artificial Intelligence & Cyber Security Lab; 0; External Collaboration; 0As much as the development of smart grid systems, accurate and reliable solar irradiance is crucial. In smart grid systems, forecasting solar irradiance remains a vital solution to mitigate the best management of energy.This study presents a new approach using a hybrid deep learning model, Conv-LSTM-AE, enhanced by Bayesian algorithm optimization. In four seasons of the years, the model was evaluated based on the accuracy using different metrices errors, and time computing in training. The model exhibits exceptional adeptness as compared to other deep learning models, showing that the Conv-LSTM-AE with Bayesian optimization is a standout performer with remarkable accuracy. This development represents a substantial step towards utilizing state-of-the-art technology for the efficient use of solar power within intelligent grid systems.Publication Rechargeable Battery State Estimation Based on Adaptive-Rate Processing and Machine Learning(IEEE, 2023) Alyoucef, Afnan; Mian Qaisar, Saeed; Hafsi, Meriem; 1; Electrical and Computer Engineering; Alyoucef, Afnan; Energy Lab; 1; Department Collaboration; External Collaboration; 0The generalization of the use of electronic systems and their integration in industrial systems and different aspects of modern life (internet of things, electric vehicles, robotics, smart grids), give rise to new challenges related to the storage and optimized management of energy. Lithium-on batteries perfectly meet this objective due to their good qualities such as high energy density, small installation size, low self-discharge and high supply capacity. However, their wide application requires further research on battery failure prediction and health management. Intelligent “battery management systems” (BMSs) employ the real-time estimation and control algorithms to improve the battery safety while enhancing its performance. Nevertheless, BMS are complex and require increased processing power which could lead to more power consumption. In this context, the present article provides a new approach for efficient prediction of the “Lithiumion” (Li-ion) battery cells capacities by analysing and exploiting the battery parameters, acquired by an event-driven module. It acquires the intended cells voltages, currents and temperature values during the charge-discharge cycles. The solution is based on the machine learning algorithms and event-based segmentation. The “National Aeronautics and Space Administration” (NASA) has provided a high-power Li-Ion cells dataset for the purpose of research and innovation. This dataset is used to test and evaluate the suggested approach. The evaluation of the overall performance of the chain has shown encouraging results of the proposed approach.Publication Design of receiver RF front end for mm-Wave 5G applications(IEEE, 2024-03-21) Hussein, Aziza; Nora Alamoudi; Mirna Ali; Ahmed Zakaria; M. Mourad Mabrook; 1; Electrical and Computer Engineering; Aziza I. Hussein; NA; NA; External Collaboration; 1Recently, the research emphasis has shifted towards 5G due to its potential to accommodate the increasing demand for data traffic, extensive interconnectivity of devices, and the emergence of numerous novel applications. Given the inadequacy of speed provided by spectrum resources in the lower frequency bands of 5G, a novel sub-generation utilizing millimeter wave frequencies has been introduced. The new sub-generation is called mm-wave 5G and provides higher bandwidth for faster speed and higher capacity. RF system architecture, circuits, and antenna innovation will be required to provide the requisite speed and capacity. In this paper the design challenges and trade-offs in RF front-end circuits and receiver sub-systems are discussed. Moreover, the massive multiple-input multiple-output (MIMO) techniques are examined. In addition, design, and simulation of key components in 5G mm-wave receiver, including the design of linear phased array antenna receiver and an analog-to-digital converter (ADC) is presented. Sigma-Delta (ΣΔM) Converters are a type of ADC with a technique that involves oversampling and shaping quantization noise to achieve a higher resolution which make it a best choice to be used in the design of the mm wave 5G communication applications. The proposed ΣΔM uses multistage architecture to provide high-order noise shaping and high resolution. The simulation results shows that the designed Cascade 2-2 MASH ΣΔ achieve a signal to noise ratio of 110(resolution of 16 bits).Publication Accurate Classification of Cervical Cancer Based on Multi-layer Perceptron Hunger Games search Optimization technique(IEEE, 2024-03-21) Rabab Hamed M. Aly; Hussein, Aziza; Rasha Y. Youssef; NA; Electrical and Computer Engineering; Rabab Hamed M. Aly; NA; NA; External Collaboration; NACancer is distinguished by the presence of abnormal cellular proliferation and growth, both of which serve as signs and symptoms for this kind of illness. Computer vision, deep learning, and metaheuristics optimization techniques are increasingly important for solving complex medical Artificial Intelligence (AI) problems such as cancer detection. This paper introduces a new methodology for training the Multi-Layer Perceptron (MLP) using the optimization algorithm known as Hunger Games search Optimization technique (HGO) and apply this method to classify the cervical cancer. The main goal of this method is to reduce the error and enhance the classification rate of cervical cancer. The outcomes show that the MLP with HGO algorithm performed better than other algorithms in terms of classification efficacy and accuracy rate. Simulation outcomes indicate that the proposed strategy performs better than previously published research in terms of effectiveness for the classification optimization methods.Publication Deep Learning for Fault Detection of Digital VLSI Circuits(IEEE, 2024-03-21) Lamya Gaber; Hussein, Aziza; NA; Electrical and Computer Engineering; Lamya Gaber; NA; NA; External Collaboration; NAWith the increasing complexity and scale of digital VLSI designs, ensuring reliability in IC design necessitates effective fault detection processes during the pre-silicon stage. Many fault detection algorithms lead to significant computational time due to the problem of search space explosion. To handle the ever-growing volume of data, deep learning algorithms, a subset of machine learning techniques, can be employed. In this paper, we propose two novel approaches for fault detection (FD) of digital VLSI circuits, specifically targeting stuck-at faults. The first proposed model is semi-supervised FD model that aims to mitigate the search space explosion issue by leveraging both unsupervised and supervised learning processes. The second presented model is based on an optimizer for finding the appropriate configurations for detecting stuck-at faults in digital circuits. The initial proposed model achieves maximum validation accuracy of approximately 98% applied to circuits from ISCAS’85. This model yields a higher accuracy compared to the second approach that achieves maximum accuracy of around 95% when applied to the same circuits from the ISCAS'85 benchmark.Publication Reconfigurable Multiband Matching Circuit Design for a Non-resonant Antenna in Wireless Point-of-Care Biosensors(IEEE, 2024-03-21) Ghadah S. Alyami; Sunday Ekpo; Hussein, Aziza; NA; Electrical and Computer Engineering; Ghadah S. Alyami; NA; NA; External Collaboration; NAPoint-of-care (PoC) test is a portable test that allows patients and healthcare practitioners to perform ondemand real-time tests outside the laboratory and hospital environments. Its widespread adoption in healthcare systems enhanced the quality of care by reducing errors and providing rapid results which allow for faster treatment decisions. Adding wireless connectivity to PoC systems provides secure and instant wireless access to patients’ vital signs by healthcare professionals regardless of their location, as they can provide real-time remote monitoring, diagnostic and emergency services for patients. This paper presents a design of a reconfigurable impedance matching network (RIMN) for a non-resonant antenna chip to be implemented in wearable biosensors utilized in PoC tests. The purpose of the RIMN is to match the antenna chip’s impedance to the Industrial, Scientific and Medical (ISM) frequencies, viz: 900 MHz and 2.4 GHz. Return losses at below -10 dB with high bandwidths (of 200 MHz) and radiated power efficiencies (of over 95 %) have been achieved. The proposed mathematically modeled integrated hybrid wireless RFPerovskite energy harvester for multipurpose 5G/6G/Wi-Fi PoC biosensors promises superior performance metrics (including over 78% energy-harvesting efficiency) than the conventional approaches.Publication Maximum Power Point Tracking Controller with Neural Gas Network PID of Boost Converter(IEEE, 2024-03-21) Rabab Hamed M. Aly; Hussein, Aziza; NA; Electrical and Computer Engineering; Rabab Hamed M. Aly; NA; NA; External Collaboration; NAEach photovoltaic (PV) system demonstrates a specific point on the current-voltage (IV) curve characteristics where the power generated reaches its maximum value which is referred to as the maximum power point (MPP). The maximum power point tracking (MPPT) system is designed to sample the output of solar cells and adjust the load in order to achieve maximum power output under various environmental conditions. In this paper, a design of a boost converter with MPPT based on a PID controller is proposed. The goal is to optimize the extraction of the highest attainable power from photovoltaic systems. The extracted power is subsequently directed to the load through a boost converter, which elevates the voltage to the necessary level. The PID controller is calibrated by Neural Gas Network (NGN) and utilized to achieve a consistent output voltage irrespective of fluctuations in the power source or the connected load. Moreover, an NGN technique is applied to generate the optimal PID parameters before using the PID. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system.Publication Fault Detection in Analog/Digtal VLSI Circuits based on Artificial Intelligence(IEEE, 2024-03-19) Lamya Gaber; Hussein, Aziza; NA; Electrical and Computer Engineering; Lamya Gaber; NA; NA; External Collaboration; NADue to the explosive demand and rapid development of the electronics industry, the integration and complexity of electronic devices have significantly grown. Fault detection has played a vital role in identification of faults in electronic circuits, ensuring normal operation and reliability of systems. Given traditional methods of fault detection are often inaccurate and time-consuming, artificial intelligence techniques are a growing interest for researchers of this field. In this paper, various artificial intelligence-based fault detection techniques in analog and digital VLSI circuits are described. In addition, two distinct models for fault detection of digital circuits based on deep learning are proposed. The primary goal of the first approach is utilization of a stacked sparse autoencoder to avoid the search space explosion problem. The second proposed method utilizes an optimization method for detecting the best model configuration. The proposed models deliver maximum validation accuracy of 97.7% and 95.7% respectively, implemented on digital circuit from ISCAS’85.Publication VVoIP Quality Improvement Over IMS Network based on background subtraction(IEEE, 2024-03-21) Hanafy M. Ali; Hussein, Aziza; Waled M. Ismail; Yahia B. Hassan; NA; Electrical and Computer Engineering; Hanafy M. Ali; NA; NA; External Collaboration; NAThe dependence on making Voice and Video over Internet Protocol (VVoIP) calls has increased dramatically in recent times. The use of VVoIP applications is not limited to social applications only but extended to business applications. The improvement of VVoIP call quality is one of the most important factors that enhance the spread of VVoIP services. In many cases, the high network traffic prevents the possibility of sending high data rate live video streams as sending high data rate live stream over high-traffic networks causes an increase in the data loss rate.In this paper, the codec switching techniques used to improve the quality under live networks with high traffic are discussed. In addition, A technique based on background subtraction to improve the quality of VVoIP calls using Python code to detect the moving pixels (foreground) and subtract them from the frame to discard the background is proposed.The proposed technique was tested on large scale live network. The tested parameters impacting the Quality of Service (QoS) were packet loss and delay variation (Jitter). The study based on the comparison of packet loss and Jitter for the three common resolutions 480P, 720P and, 1080P over high traffic network without using background subtraction vs. using background subtraction. The results showed a significant improvement in different rates for each resolution and network condition.Publication High-Performance and Energy-Efficient FIR Filter Architecture Using Parallel Prefix Adder-Based Triangular Common Subexpression Elimination Algorithm for IoT Enabled Wireless Sensor Network(IEEE, 2023-10-10) Mohammed, Abdul Majid,; Raghavendra., D. Kulkarni; Edrees Yahya, Alhawsawi; J Charles, Kumar; 0; Electrical and Computer Engineering; Charles, Rajesh Kumar; Energy Lab; 0; External Collaboration; 0Wireless sensor networks (WSN) generate an enormous quantity of data, which necessitates preprocessing at the source to lower the entire amount of data collected for transmission and storage. The finite impulse response (FIR) filter is extensively utilized as a signal preprocessing phase in WSNs. The triangular common subexpressions elimination framework is suggested in which the number of logical operators (LO) and logical depths (LD) in FIR filter implementations has been significantly reduced. In the presented Triangular CSE approach, the occurrences of common Triangular subexpressions are first examined across the complete set of filter coefficients. The proposed method considerably reduces the computational burden, which is again reduced by using Vertical common expressions and Horizontal common expressions. The paper includes a detailed illustration of the algorithm and compares existing algorithms. The proposed architecture minimizes the delay units, structural adders, and adder circuits in the FIR filter's multiplier blocks (MBAs) to decrease the overall complexity of the hardware. Furthermore, the design is improved using Parallel Prefix Adder Based on Kogge-Stone Tree. The LD is not increased during the reduction in LO. Energy consumption is also investigated, along with hardware expenses. Compared to other methods, the proposed solution decreases the number of structural adders while slightly increasing the number of delay elements.Publication Fire Detection and Seeing the Invisible Through Smoke Using Hyperspectral and Multi-Spectral Images(IEEE, 2023-11-10) Alboody, Ahed; Mian Qaisar, Saeed; Roussel, Gilles; 0; Electrical and Computer Engineering; Alboody, Ahed; Artificial Intelligence & Cyber Security Lab; 0; External Collaboration; 0The global warming has serious impact on our climate. Due to this, the frequency and the intensity of forest fires is increasing. It has shown serious challenges such as the protection of resources, human and wild life, health, and property. This study focuses on developing an artificial intelligence assistive innovative solution for active fire detection in the context of smart cities and vicinities. This paper addresses spectral analysis, detection and classification of active fires and seeing the invisible through smoke and thin clouds. The appealing applications are in urban surveillance, smart cities, future industries, forests and earth observation. The idea is realizable by using an intelligent hybridization of machine/deep learning models and using multi-sensor images (aerial, satellite). For this purpose, we use hyperspectral images (Visible, Near Infra-red (NIR) and Short-Wave Infrared (SWIR)) from AVIRIS aerial and Multi-Spectral Sentinel-2 satellite images. AVIRIS images are 224 spectral bands of wavelengths with a spatial resolution of 15 meters, which varies from 366nm (nanometers) up to 2500nm. However, AVIRIS image studied for their spectral richness of wavelengths not yet completely exploited by machine and deep learning and in SWIR to detect active fires. While, Sentinel-2 image has 13 spectral bands (Visible, NIR and SWIR) with three spatial resolutions (10, 20 and 60 meters). First, we explain and describe the preparation phase of hyperspectral and multispectral image databases of forest fires. These databases contain hyperspectral and multispectral endmembers data of different sites for forest fires. Then, we conduct a spectral analysis from these endmembers to characterize the hyperspectral/multispectral reflectance of active fires to identify the distinct wavelengths for fire detection. We identify the wavelengths that can be used for an effective identification of fire and to see through fires smoke and thin clouds. Onward, the selected feature set is processed by robust machine/deep learning algorithms and their performance is compared for automated identification of fire and invisible vision amelioration. The proposed machine/deep learning method secured an overall test accuracy of 99.1%.Publication Exploring the Maze: A Comparative Study of Path Finding Algorithms for PAC-Man Game(IEEE, 2024-03-21) Salem, Nema; Haneya, Hala; Balbaid, Hanin; Asrar, Manal; 0; Electrical and Computer Engineering; Salem, Nema; Artificial Intelligence & Cyber Security Lab; 0; No Collaboration; 3Artificial Intelligence (AI) has become an integral part of our lives, finding applications across various industries. Search algorithms play a crucial role in AI. This paper focuses on the comparison of different search algorithms within the context of path-planning in the UC Berkeley’s PAC-Man’s game. The algorithms under consideration include Depth-First Search (DFS), Breadth-First Search (BFS), Uniform Cost Search (UCS), Iterative Deepening Depth First Search (IDDFS), and A⇤ Search. The objective is to identify the most effective algorithm in terms of path-finding performance. The study’s findings reveal that the A⇤ search algorithm outperforms the others in terms of score, cost, and node expansion, making it the most suitable choice for finding the shortest path in the PAC-Man’s game.