Yazar "Ahmed, Saadaldeen Rashid" seçeneğine göre listele

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    A descriptive statistical analysis of overweight and obesity using big data
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ali, Salam Abdulabbas Ganim; Al-Fayyadh, Hayder Rahm Dakheel; Mohammed, Shaimaa Hadi; Ahmed, Saadaldeen Rashid
    In this paper, we have obtained the dataset from an open-source repository for obese people by focused on a descriptive statistical analysis of overweight and obesity using big data. We performed the statistical analysis on large scale streaming data for obesity prediction. We have classified the obesity with all categories on the scale of Body Mass Index (BMI) is being calculated i.e., underweight, normal weight, overweight, obese, very obese, and extremely obese using MapReduce technique with the help of Apache Spark and Apache Hadoop engine in pydoop python programming. The MapReduce technique in-volves the updating of cluster centers after arrival of new batch in the stream of data. The streaming of data is produced by the sensors which are classified into six different BMI categories, which are stored and processed through big data tools connected to the statistical analysis system. The Apache spark produces the latency values in accessing the data from dataset. We analyzed any obesity in the people from the normal latency value using the Apache spark and Hadoop which are well known in big data. The methods and techniques by which we can predict obesity efficiently from the large-scale streaming data has been per-formed using python programming. This is applied with the help of Apache Spark and Hadoop. In order to validate the efficiency of MapReduce technique. We have tested it both on single and distributed environment for obesity prediction using the built-in Pydoop package in python.
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    Android application to retrieve car details from car plate numbers
    (IOP Publishing Ltd, 2020) Ahmed, Saadaldeen Rashid; Ahmed, Mohammed Rashid; Majeed, Doaa Abdulwahhab; Hammed, Azal Hazim; Daham, Asayel Salam; Hammed, Elaf Hazim
    Computer vision is a field in computer science that has had great success due to the increasing popularity of machine learning. Instead of having a human look at images and decide what they depict, we are able to teach computers to recognize patterns of previous images and see the resemblance in new images. In this paper we will get car all information from his number plates through an Android Application which use image preprocessing and YOLO (You only look once) technique to detect the plates and then optimal character recognition to read the plates and Arabic numbers of the cars in Iraq and we have trained it using darkflow. Every Car has its unique Licensed number plate which will be scanned and give the details about the car. Computer vision can also be used to read alphanumeric characters in images and turn them into text. We have implemented API to detect the text and number in the plates. The purpose of this project is to develop a system for our Government and Police So that If police need details about any car he would easily access it. We have achieved the accuracy of 92.23% which means out of 3500 number plates 3220 read correctly. © 2020 Published under licence by IOP Publishing Ltd.
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    Design and fabrication of UWB microstrip Antenna on different substrates for wireless Communication system
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, Olaf; Thaher, Raad H.; Ahmed, Saadaldeen Rashid
    A new shape of microstrip antenna is proposed that operates in the 5G Sub band with size 26times 30times 1-6)mm3 ' and the effect of changing the suberate on the performance of the antenna was Investigated; the antenna is Installed using the Roger 8850 Substrata with relative dielectric Constant in_mathrmr=2,2 and loss tangent tan partial=0.009 The Effect of Changing the Substrate t_0in_r=4.3, in_r=6,15) is studied, it is found that these substrates affect the return loss (S11), gain, and group delay. It is found that in_r=2,2 provides better return loss and group delay, whereas the Substrate with in r=6,15 gives better gain; therefore, the Substrate selection depends on the required performance factor. The antennas were fabricated using Rogers Substrata with a relative dielectric constant of in_r=2,2 and tested using the Vector Network Analyzer; it was found that the Simulation and experimental results are in reasonable agreement.
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    Detecting and classifying drug interaction using data mining techniques
    (Institute of Electrical and Electronics Engineers Inc., 2022) Yaseen, Baraa Taha; Kurnaz, Sefer; Ahmed, Saadaldeen Rashid
    Thousands of licensed drugs are now available to patients. The Warnings and Precautions section of a medication's label was created to identify and characterize severe or clinically significant adverse reactions and other potential safety issues. This method gathers a patient's gender, age, pregnancy status, and current health issues. The suggested method involves data collecting and analysis. We visited DINTO, DrugBank, RxNorm, and the FDA while gathering data. Test findings showed the suggested instrument was accurate (96%) and efficient.
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    Efficient PV monitoring with LoRaWAN: integrating IoT and smartphone application for cost-effective solutions
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hameed, Bilal Hashim; Kurnaz, Sefer; Ahmed, Saadaldeen Rashid
    This paper presents a low-cost, scalable approach for monitoring photovoltaic (PV) installations, catering to the growing demand for effective monitoring frameworks in renewable energy. The system incorporates sensors to record critical data such as solar panel output, battery status, and ambient variables, addressing cost-effectiveness, scalability, and remote monitoring concerns in PV installations. Data transfer is supported via a LoRaWAN network, providing long-range and low-power connectivity, making it ideal for remote and off-grid PV systems. Field testing results show that the system provides real-time data on PV system performance, enabling preventive maintenance and informed decision-making. The inclusion of LoRaWAN technology improves system scalability and efficiency, providing a path for mass adoption in sustainable energy solutions. The low-cost design makes PV systems accessible to a wide range of users, including those in distant or economically challenged locations. The research describes a realistic and efficient monitoring approach customized to the unique requirements of PV systems, contributing to the greater adoption of sustainable energy solutions by using LoRaWan technology.
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    Optimizing hybrid energy systems: employing smart monitoring networks with IoT integration
    (Institute of Electrical and Electronics Engineers Inc., 2024) Taher, Anmar Yahya; Kurnaz, Sefer; Ahmed, Saadaldeen Rashid
    For effective and sustainable energy solutions, this paper investigates at a hybrid energy system that uses Internet of Things technologies. Three components make up the system: PV system, an AC turbine voltage source, DC batteries, and ThingSpeak, an IoT analytics program hosted on the cloud that allows for real-time monitoring. In order to optimize performance, the study emphasizes the significance of hybrid energy systems as well as the requirement for improved analytics and monitoring. A detailed description of the experimental setup is given, including the measured parameters. The results demonstrate the hybrid system's adaptability to various loads and input voltages. fluctuations in DC battery and turbine voltages cause fluctuations in AC power and AC current, while the AC voltage stays constant at 224.4 V. For efficient analytics and monitoring, the hybrid energy system must include ThingSpeak. The study emphasizes how the system can adjust to changing conditions and how Internet of Things technologies may assist hybrid energy systems grow more powerful. This research adds to the current discourse on the integration of IoT data for enhanced system optimization and sustainable energy solutions.
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    SPEAKER IDENTIFICATION MODEL BASED ON DEEP NURAL NETWOKS
    (College of Education, Al-Iraqia University, 2022) Ahmed, Saadaldeen Rashid; Abbood, Zainab Ali; Farhan, hameed Mutlag; Yasen, Baraa Taha; Ahmed, Mohammed Rashid; Duru, Adil Deniz
    This study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker's presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases. © 2022 Iraqi Journal for Computer Science and Mathematics. All rights reserved.