Author: Dr.Ammar Falih

Abstract One of the most common causes of functional frailty is major depressive disorder (MDD). MDD is a chronic condition that requires long-term therapy and professional assistance. Additionally, MDD effective treatment requires early detection. Unfortunately, it has intricated clinical characteristics that make early diagnosis and treatment difficult for clinicians. Furthermore, there are currently no clinically effective diagnostic biomarkers that can confirm an MDD diagnosis. However, electroencephalogram (EEG) data from the brain have recently been used to make a quantitative diagnosis of MDD. In addition, As being among the most cutting-edge artificial intelligence (AI) technologies, deep learning (DL) has exhibited superior performance in a wide range of real-world applications, from computer vision to healthcare. However, an additional challenge could be the extraction of information from the ECG raw data. This paper presents a method for converting EEG data to power spectral density (PSD) images, and then they were classified as healthy or MDD using a deep neural network for feature extraction and a machine learning (ML) classifier. When employing the proposed approach, the images formed from the PSD show a considerably improved performance in classification results. Keywords Artificial intelligence; Electroencephalogram; Machine learning; Major depressive disorder; Power spectral density https://ijeecs.iaescore.com/index.php/IJEECS/article/view/29130

Abstract This research is an exploration into developing a system for enabling Radio Frequency Identification (RFID) labels to be connected to the Internet while taking into account their unique impediments. Additionally, this mechanism enables the tag to be extraordinarily distinct and spoken to as a communication material capable of communicating with other participants, which can facilitate and rearrange the use of the “Internet of Things” concept in the not-too-distant future. To build a mechanism capable of connecting RFID labels to the Internet. The methods taken by various researchers are investigated and dissected, enabling a better understanding of the difficulties and shortcomings associated with RFID labels connected to the Internet. The analysis and examination have resulted in the creation of another system that allows use of TCP/IP. The structure established in this paper is predicated on the capability of RFID labels to be used as procedures (TCP forms) within a host. As a result, each procedure has a procedure ID or port number, which enables various members to identify and communicate with the tag through the process ID. This is accomplished through a built-in interpretation portion that converts the RFID tag’s authentic personality (ID) to a new ID that can be recognized as a TCP port number. The results of this paper show that the system worked effectively for the purpose for which it was designed. The results show that the actualized system enables RFID labels to be linked to the Internet and to be exceptionally distinct. Additionally, it enables labels to send and receive information and guidance outside of the RFID system, through the Internet, and from various members. The framework’s success would provide several experts with opportunities to actualize the concept of “Internet of Things.” Keywords 1 Smart, network, algorithm, RFID, TCP/IP, IoT. CEUR-WS.org/Vol-3149/paper4.pdf

Abstract Argumentation and inference is a procedure for understanding the problem and solving it in the machine. Here, the machine is meant to be any computer system that depends on artificial intelligence to understand the environment and interact with it. One of important issue in the cloud-based systems that are seen in all of them is load balancing. Load balance is an important and challenging problem in cloud computing. Our purpose in this study is to consider the two important parameters of the processor efficiency of the node and the time delay between the order and the server in terms of geographical distance, using automated Fuzzy Inference Intelligence argumentation and inference, we have the advantage of using uncertain inputs, and we can maximize system performance for clouds with different sizes and achieve better load balances. We first have some related research to determine the innovation of the present study. The results show that using fuzzy argument and fuzzy inference, we have been able to significantly reduce the response time and also the results indicate that the distance between the customer and the destination node is the parameter that has the most effect on the efficiency of the proposed model Keywords Automated Reasoning, Automated Deduction, Cloud-based, Computing https://iaeme.com/Home/article_id/IJCIET_09_10_214