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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 Whether it's revitalizing outdated interiors, conceptualizing cutting-edge office spaces, or curating luxurious residential retreats, I approach each project with enthusiasm and dedication.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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This simple Python software is designed to assist Civil and Geotechnical Engineers in performing site-specific seismic hazard assessments. The program calculates the seismic response spectrum based on user-provided geotechnical and seismic parameters, generating a comprehensive technical report that includes the response spectrum data and figures. The analysis adheres to Eurocode 8 and the Greek Annex, ensuring compliance with international standards for earthquake-resistant design.

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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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