Quantum Computing Approaches for High-Speed Visual Search Haewon Byeon, A. Phani Sheetal, Musaddak Maher Abdul Zahra, Mukesh Soni, Rajit Nair, Abhishek Jain 2025 International Conference on Networks and Cryptology Netcrypt 2025, 2025 This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100-150 ~\text{ms}$</tex>. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves.
Graph-Based Deep Learning for Brain Network Analysis and Connectivity Mapping Haewon Byeon, Mungara Kiran Kumar, Musaddak Maher Abdul Zahra, Mukesh Soni, Ramgopal Kashyap, Abhishek Jain 2025 International Conference on Networks and Cryptology Netcrypt 2025, 2025 This paper introduces graph-based deep learning for functional connectivity mapping and brain network analysis that improves accuracy, reliability, and efficiency. The method creates a link network from functional MRI data, with nodes indicating brain areas and lines exhibiting statistical connectivity. The model strengthens neural linkages through spectrum decomposition, graph convolution layers, and attention processes to improve categorization. It outperforms CNNs (89.2%), RNNs (85.6%), SVM (80.4%), and RF (78.1%) with 95.8% accuracy. It is fast (120 ms), scalable, and easy to grasp (9/10), making it ideal for large-scale scanning. The approach also detects abnormalities in neuron joining, which may aid neurological disorder diagnosis. Precision (94.5%), recall (96.2%), and F1-score (95.3%) demonstrate its dependability. It utilizes less memory than CNNs (400 MB) and RNNs (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 5 0 ~ M B}$</tex>) at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 5 0 ~ M B}$</tex>. This study shows that graph-based deep learning works effectively in functional connectivity research. They help us understand brain-computer interfaces, cognitive function, and neurological diseases.
Ensuring Informed Consent and Fair Access to Artificial Intelligence Enhanced Healthcare Services Musaddak Maher Abdul Zahra, Khyati Nema, Rajit Nair, Bibhuti Bhusan Dash, Subrata Chowdhury, Sudhansu Shekhar Patra 2025 6th International Conference for Emerging Technology Incet 2025, 2025 This article discusses a novel strategy to make AI-based healthcare informed consent clearer, simpler to obtain, and more equitable. The recommended solution uses natural language processing and user input to process permission forms faster and make them easier to understand for patients. We verify the shorter documents for clarity, user comprehension, and fairness, ensuring the agreement process is effective for numerous individuals. Dynamic feedback allows the consent process to improve depending on user behavior and group-specific issues. The strategy uses continual growth and monitoring to eliminate bias, clarify materials, and ensure compliance. Performance measuring metrics improve reading scores, user comprehension, validation accuracy, and fairness measures compared to previous techniques. The proposed method simplifies health care and makes consumers happy. These modifications ensure that informed consent in healthcare is lawful, fair, and adaptable, enabling a patient-centered strategy that adjusts to changing healthcare requirements. This study suggests that employing modern technology in informed consent might make patients more confident and participating, creating a more inclusive healthcare system.
Experimental analysis of corrugated solar still using nano-enhanced phase change material Prakash Patel, Zulfiquar N. Ansari, Musaddak Maher Abdul Zahra, Ghanshyam G. Tejani, Subhav Singh, Deekshant Varshney, Mohd Asif Shah Multidisciplinary Science Journal, 2025 The objective of this research work is to evaluate the influence of v-shaped corrugated basin integrated with nano-enhanced phase change material (NePCM) on the freshwater productivity. Experiments were conducted using different nanoparticles such as graphene oxide (GO), Al2O3, TiO2 and CuO as well as phase change material (PCM) namely fatty acids and graphene oxide. The conventional single slope solar still (CSSS) was combined with individual nanoparticles (CSSS + nanoparticle) and PCM (CSSS + PCM) and also with their combination (CSSS + nanoparticle + PCM) for performance analysis. The consideration of nanoparticle and PCM either individually or in combination increased the daily yield in comparison to the CSSS. The highest productivity was obtained in the case of CSSS + Paraffin wax + Al2O3 + TiO2 + GO (5.21 L/m2/day) as compared to the CSSS (3.15 L/m2/day), increasing the daily yield by 65%. The daily yield in case of CSSS + Fatty acid + Al2O3 + TiO2 + GO is 5.09 L/m2/day over the CSSS, thereby resulting in productivity improvement by 61%. The findings of the study present the potentiality of the developed v-shaped corrugated solar still with different combination of nanoparticles and PCM.
Investigation on fixed pitch Darrieus vertical axis wind turbine Ramesh K. Kavade, Mahesh M. Sonekar, Dilip S. Choudhari, Prateek D. Malwe, Nitin P. Sherje, Mushtaq Ahmad Ansari, Mohd Asif Shah, Musaddak Maher Abdul Zahra, Abhinav Kumar Aip Advances, 2024 Small-scale Darrieus wind turbines have a wide scope in areas that are isolated from the power grid for such small-scale household applications. Applications of wind turbines on house roofs are one potential way to generate electricity from wind energy harvesting in low-wind urban locations. This work studies the aerodynamic behavior of a vertical axis wind turbine based on a MATLAB programming mathematical model. The NACA0021 airfoil profile blade was used in this present research investigation. The turbine was fabricated with dimensions such as chord length, c = 95 mm, blade height, h = 600 mm, and turbine diameter, D = 600 mm. The experimental results of the turbine for air velocity from 1 to 12 ms−1 were used in this paper and compared with analytical results. It has been observed that the fixed-pitch turbine does not start by itself at a low air velocity of 1 to 5 m/s due to a minimum and negative torque.
A mathematical model for investigation of dry band location on 22 kV bushing with and without RTV coating: Experimental study L. Kalaivani, R.V. Maheswari, Emad Makki, Bharat Singh, Sanjay B Warkad, Jayant Giri, B. Vigneshwaran, Alagar karthick, Musaddak Maher Abdul Zahra, Abhinav kumar, Hitesh Panchal Methodsx, 2024 As different pollutants are deposited on the high voltage bushings, a dry band forms, which causes a flashover. The bushing's contaminated layer will weaken its insulation and have an impact on its electrical characteristics. The performance of bushings in dry band conditions of various lengths was investigated in this proposed piece of work, and a dynamic arc model is presented for the arc process in polluted bushings. It shows satisfactory performance in modelling the arc variables for various dry band positions. The developed dynamic open model for contaminated bushings with and without RTV coating predicted the flashover voltage and dry band positions. Any type of contamination, such as sea salt, road salt, and industrial pollutants prevalent in several sites, can be studied using the established model. Ultimately, it was discovered that there was good agreement between the model's results and the outcomes of the experiments. •Mathematical modeling of 22 kV bushing is conceded out for diverse polluted dry band location at lead-in, lead-out and middle region of bushing surface.•Dynamic arc modeling involved in bushing flashover process for different dry band location is done and flashover voltage is predicted•Experimental work is carried out to find FOV for the bushing with different dry location and compared with predicted FOV.
Performance analysis of photovoltaic panel using machine learning method Ganesh S. Wahile, Srikant Londhe, Shivshankar Trikal, Chandrakant Kothare, Prateek D. Malwe, Nitin P. Sherje, Prasad D. Kulkarni, Uday Aswalekar, Chandrakant Sonawane, Mustak Maher Abdul Zahra, Abhinav Kumar Indonesian Journal of Electrical Engineering and Computer Science, 2024 Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.
Numerical analysis of the powder mixed electrical discharge machining (PMEDM) process for TZM-molybdenum superalloy using finite element method Kapil Surani, Shailesh Patel, Mathan Kumar Mounagurusamy, Musaddak Maher Abdul Zahra, Hitesh Panchal, Md Irfanul Haque Siddiqui, Mohd Asif Shah, Natrayan L, Abhinav Kumar Aip Advances, 2024 The powder mixed electrical discharge machining (PMEDM) process was simulated via finite element analysis in the current study to assess heat behavior and material removal rate. The goal of this paper is to conduct a thorough experimental and thermal examination of electrical discharge machining (EDM) in order to forecast its cutting characteristics and subsequently optimize the output variables using a response surface methodology for simulations and choosing the most suitable set of process variables related to the PMEDM process. This study’s objective is to design a 2D axisymmetrical transient thermal model that might also describe the physics of material removal in a single spark PMEDM operation on a Titanium Zirconium Molybdenum (TZM) superalloy. ANSYS (version 9.1) software is used to perform transient heat transfer simulations to determine the temperature profile with the amount of material removal at different current, pulse on and off times, gap voltages, and fraction of heat that enters the specimen. The PMEDM process produced craters with a lower diameter and depth, which increased the material removal rate and enhanced the surfacing quality. Compared to the conventional EDM process, the inclusion of powder raised the heat flux given to the work material by 10%–12%. It has been determined that with the single spark modeling technique, the temperature significantly dropped in both the radial and depth directions. The computational results are compared with experimental observations for similar machining conditions, and both results agree satisfactorily.
On Some Novel Results About Weak Fuzzy Complex Matrices Yaser Ahmad Alhasan, , , , , , Abuobida Mohammed A. Alfahal, Raja Abdullah Abdulfatah, Giorgio Nordo, Musaddak M. Abdul Zahra International Journal of Neutrosophic Science, 2023
Pump Insole with Mini AC Generator for Emergency Energy Musaddak Maher Abdul Zahra, Bashar Mahmood Ali, Hailer Sharif, Ahmed Read Al-Tameemi, Ali Al Mansor, Abdul Razzaq T. Zaboun, Saja Hameed Kareem, Ahmed A. Ali, Ilhan Garip, Karthikeyan Sathasivam Electric Power Components and Systems, 2023
AI-Based convolute Neural Approach Management To Predict The RNA Structure Saqib Rishi, Sandip Debnath, Safura Dewani, D. Stalin David, Refed Adnan Jalee, Musaddak Maher Abdul Zahra 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
Using a hybrid algorithm and feature selection for network anomaly intrusion detection Journal of Mechanical Engineering Research and Developments, 2021
Artificial intelligent smart home automation with secured camera management-based GSM, cloud computing and arduino Periodicals of Engineering and Natural Sciences, 2020