10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Sign up or log in to bookmark your favorites and sync them to your phone or calendar.
Authors - Nabaa T. Salman, Wasan A. Wali, Mohammed Lami Abstract - Solar energy is an inexhaustible source of carbon-free energy world-wide. However solar radiation determines the amount of electrical energy and current that can be produced by solar panels. In this paper, the dynamic regulation of solar radiation using Polymer Dispersed Liquid Crystal (PDLC) was studied. It regulates solar radiation that is incident on the solar cell’s surface by changing its transparency because of the applied voltage. Thus, researchers were able to obtain a variable range of solar radiation at the same time and then control the power of the solar cell produced according to the user's request. In the outdoor experiment, the behavior of filtrated radiation in daylight performance under different sky circumstances was assessed by examining solar radiation both with and without a PDLC screen. To simulate the PDLC and forecast solar radiation in real time, the researchers utilized an Adaptive Neuro-Fuzzy Inference System (ANFIS). MATLAB program utilized 3.5 W, 25 W, and 100W solar panels. PDLC transparency ranges from 5% to 83%. The results showed that PDLC overall shading on transparent\opaque states are 73% to 37% respectively at the same point where PDLC films may regulate the solar cell's output power at a pace of (39.6%, 39.5%, 42%) of the total cell power for the simulated solar panel respectively.
Authors - Miguel Angel Ruiz-Adarmes Abstract - Knowing the weather conditions at airports is of vital importance for reasons that affect the safety, efficiency, and comfort of flights. Bad weather, such as strong winds or fog, can represent a significant danger to aircraft during takeoffs, landings, and flights. Therefore, learning to predict weather behavior, based on prior information, is important. That is why this research on the prediction of weather conditions at Jorge Ch´avez Airport in Lima is presented. To do this, a set of previous data was used to which the J48, Random Forest, SVM, Bayes Net, and Neural Network algorithms were applied to identify that the Random Forest algorithm obtained the best behavior with Accuracy = 76.8004% for the training and validation process; and Accuracy = 78.5181% for the test process. As a proof of concept, a Java application was implemented.
Authors - Strahil Sokolov, Kaloyan Varlyakov, Dimitar Radev Abstract - In this paper an approach is described for improving the quality of data generated from medical screening processes based on machine learning. The designed workflow uses data acquisition from Titmus equipment, performs data preprocessing, model training and health record evaluation. We are proposing a design of a distributed system to realize this approach in order to bridge the gap between the cloud-native technologies and their usage for patient screening in rural or remote areas. The algorithm shows promising results and is suitable for implementation on Edge-AI , IoT and cloud-based medical support systems.
Authors - Marwa Mostafa Yassin, Nahla A. Belal, Aliaa Youssif Abstract - Papilledema is a medical disorder marked by the enlargement of the optic disc. Optic disk imaging is essential for the diagnosis of papilledema, as neglecting to perform this procedure can lead to fatal outcomes. This research presents a novel approach that combines deep learning with the Harris Hawks Optimization (HHO) algorithm to increase the accuracy of diagnosing and distinguishing papilledema in optical disk images. The proposed technique presented in this study focuses on optimizing the weights of the Convolutional Neural Network (CNN) model. This optimization process improves model training by using the underlying optimization principles. The technique was evaluated using the Kaggle dataset, which was made available for this purpose. The evaluation results showed that the proposed technique achieved an accuracy of 0.997%, surpassing the performance of existing techniques such as VGG16, DenseNet121, EfficientNetB0, and EfficientNetB3. The proposed model demonstrates that state-of-the-art CNN models, when paired with the HHO algorithm, can reliably diagnose real papilledema, pseudo papilledema, and normal optic discs. This could potentially save lives for patients.
Authors - Otuu Obinna Ogbonnia, Joseph Henry Anajemba, Oko-Otu Chukwuemeka N., Deepak Sahoo Abstract - Scholars have investigated the challenges of community policing (CP) in Nigeria through socio-political, economic, and cultural lenses, with none adopting a method that can reveal these challenges comprehensively. This has led to a gap in recognizing key CP problems, thereby resulting in ineffective solutions from the government, and making government services in this context less accessible and responsive to citizens. This study employed Greenhalgh’s meta-narrative approach to unveil community policing challenges that were previously overlooked in Nigerian context. Drawing from a variety of sources such as scholarly articles (ACM digital library, Science Direct), official documents, and media coverage, this study identified lack of robust technology usage, lack of citizens’ participation, citizens’ unwillingness to share information and lack of trust, accountability and transparency as major community policing challenges in Nigeria. This study contributes to a nuanced understanding of the challenges hindering the successful implementation of CP in Nigeria, highlights the implications of these challenges on the overall security landscape, and offers directions to policymakers and relevant government agencies, providing insights to the design of technological solutions for community policing in Nigeria.
