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Welcome to my portfolio! I'm Maher Othman, an accomplished Interior Design Director with a passion for creating captivating spaces that blend functionality with aesthetics. With eighteen years of experience in the industry, I've had the privilege of leading diverse projects across residential, commercial, and hospitality sectors. At the heart of my approach lies a commitment to understanding the unique needs and aspirations of my clients. I believe that every space has a story to tell, and it's my mission to translate those narratives into immersive environments that resonate with both occupants and visitors alike. With a keen eye for detail and a flair for innovation, I strive to push the boundaries of design while adhering to practical considerations and budgetary constraints. Whether it's revitalizing outdated interiors, conceptualizing cutting-edge office spaces, or curating luxurious residential retreats, I approach each project with enthusiasm and dedication. Throughout my career, I've cultivated a collaborative mindset, working closely with clients, architects, contractors, and artisans to bring visions to life. From initial concept development to final implementation, I prioritize clear communication, meticulous planning, and seamless execution to ensure the success of every endeavor. In this portfolio, I invite you to explore a curated selection of my most notable projects, each offering a glimpse into my design philosophy, creative process, and commitment to excellence. From striking visual transformations to thoughtful spatial arrangements, I hope these examples inspire and resonate with you. Thank you for considering my work. I look forward to the opportunity to collaborate on your next design venture and bring your vision to fruition. Warm regards, Maher Othman Interior Design Director

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Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.

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The objective is to design and develop an application that will help to imp rove the efficiency and effectiveness of the police security measures in keeping criminal records. ABSTRACT The victims can file the FIR through the website under various sections. The user can send photo evidence if any online. There will be an 'SOS 'capability in which, the user can press a button and his/her location will be sent to the nearest Police station. There will be a separate component for the accident victims so that FIR can be registered fast and treatment can be started as soon as possible. In this current system, user's information will be kept confidential and only users complain will be forwarded to its nearest police station. Users complain number will be forwarded from the server side automatically. For identifying location and authentic person, concept of cookies and IP addressing has been used. To eliminate the location conflicts between police station, server will play a vital role. It will search the address table using IP address and forward message to that police location from where the message has been received. We intend to create a project which will help bridge the gap between the police department and the common man. 'Online FIR Registration and SOS system' project will have an app from where the users can file FIR against the offender under the various sections. The main site will be maintained by the admin (from the police) who will then notify the user if the FIR has been registered and the necessary action has been taken.

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We investigated the applicability and efficiency of the MLMC approach to the Henry-like problem with uncertain porosity, permeability and recharge. These uncertain parameters were modelled by random fields with three independent random variables. Permeability is a function of porosity. Both functions are time-dependent, have multi-scale behaviour and are defined for two layers. The numerical solution for each random realisation was obtained using the well-known ug4 parallel multigrid solver. The number of random samples required at each level was estimated by calculating the decay of the variances and the computational cost for each level. The MLMC method was used to compute the expected value and variance of several QoIs, such as the solution at a few preselected points $(t,\bx)$, the solution integrated over a small subdomain, and the time evolution of the freshwater integral. We have found that some QoIs require only 2-3 mesh levels and samples from finer meshes would not significantly improve the result. Other QoIs require more grid levels.

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According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

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In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decisionmaker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.

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