3D model of steel building

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Jovan Kostic

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Engineering

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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.

Seizure stage detection of epileptic seizure using convolutional neural networks

IJECEIAES

Dynamo Scripts for Task IDs and Space Naming.pptx

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handbook on reinforce concrete and detailing

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Exploring AI's Impact: Key Features in Due Diligence In the realm of due diligence, AI emerges as a game-changer, revolutionizing traditional methods with its advanced features. AI-powered algorithms excel in data analysis, swiftly sifting through vast amounts of information for crucial insights. Automation streamlines document review processes, ensuring accuracy and efficiency. Moreover, AI enables predictive analytics, forecasting potential risks and opportunities with precision. With machine learning capabilities, AI continuously improves its performance, adapting to evolving trends and patterns. By integrating AI into due diligence practices, businesses gain a competitive edge, maximizing efficiency and making informed decisions swiftly. AI in due diligence is not just a tool; it's a transformational force driving businesses into the future. https://www.leewayhertz.com/ai-in-due-diligence/

Artificial Intelligence in due diligence

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electrical installation and maintenance.

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This presentation, crafted with expertise from the oil and gas industry, details the imperative techniques and tools used in the process of incident investigation. The presentation highlights the structured methods involving interviews with engaged parties, the importance of gathering documentary evidence, employing structured forms and checklists, the value of reviewing surveillance footage, the power of modern technology, and synergizing with subject-matter experts. It is designed for industry professionals keen on enhancing safety practices, regulatory compliance, and preventive measures within their operational environment. Valuable for health & safety officers, compliance managers, and operation leaders, it prioritises continuous learning and adherence to best practices to ensure workplace safety and operational excellence.

Maximizing Incident Investigation Efficacy in Oil & Gas: Techniques and Tools

soginsider

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

Maher Othman Interior Design Portfolio..

MaherOthman7

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

Developing a smart system for infant incubators using the internet of things ...

IJECEIAES

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