Yazar "Salih, M.M.M." seçeneğine göre listele

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    Emotion Recognition of Humans using modern technology of AI: A Survey
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alheeti, A.A.M.; Salih, M.M.M.; Mohammed, A.H.; Hamood, M.A.; Khudhair, N.R.; Shakir, A.T.
    This comprehensive investigation and evaluation of the subject matter of emotion recognition is presented, focusing on the broader implications for society at large. The study utilizes a thorough analysis of scholarly literature and practical observations to establish a theoretical framework that facilitates comprehension of the topic under investigation. The findings have considerable implications for future investigations and pragmatic applications. Emotion recognition holds immense importance in diverse domains such as human-computer interaction and healthcare. The analysis of techniques used for emotional recognition includes facial expression assessment, speech patterns analysis, physiological signal interpretation, music perception, and written expression evaluation. The study presents a comprehensive overview of research methodologies commonly used in emotion recognition, discussing datasets, feature extraction techniques, and classification algorithms. The analysis of challenges and limitations pertaining to emotion recognition systems, including privacy concerns, is also discussed. Performance evaluation is analyzed through various methods, including machine assessment and self-report. The significance of continued investigation within the domains encompassing data integration across diverse modalities, the creation of robust classification algorithms, and the exploration of the intricate connection between the brain and affective states is underscored. © 2023 IEEE.
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    Neural Network Approach for Classification and Detection of Chest Infection
    (Institute of Electrical and Electronics Engineers Inc., 2022) Salih, M.M.M.; Cakmak, M.
    Advances in computer technology have had a profound impact on our lives and the way we see the world. The healthcare industry is advancing thanks to the use of cutting-edge computer technology, which has transformed how numerous ailments are diagnosed and treated. The number of people suffering from chest-related illnesses is increasing at an alarming pace as a result of a wide range of conditions, including air pollution. Medical applications of image processing have emerged due to data collection tool development. It is now possible to make out the diagnosis through study of the features from medical investigation reports for a group of patients. That reduces the time and cost of the diagnosis, which may help plenty of people who are unable to access regular medical facilities due to intolerable cost. In this paper, automatic chest infection diagnosis is being diagnosed using a Neural Network. Two models are used, namely the Artificial Neural Network and the CNN Neural Network. The models are tested using NIH x-ray chest image data. Results are reported with 96.7% and 99.20% accuracy from the Artificial Neural Network and CNN, respectively. © 2022 IEEE.