Browsing by Author "Shamim, S.M."

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    Identification of Drug and Protein-Protein Interaction Network Among Stress and Depression: A Bioinformatics Approach
    (Elsevier, 2023-01-18) Basar, Md. Abul; Hosen, Md. Faruk; Paul, Bikash Kumar; Hasan, Md. Rakibul; Shamim, S.M.; Bhuyian, Touhid
    The fields of data mining, computational biology, and statistics have been combined to form the massive research area of bioinformatics. In the areas of genetics, education, and healthcare, bioinformatics integrates the tools available in different fields such as computing, inventorying, performing statistical analyses, and collecting and processing genomic data. Stress and depression are two of the most severe mental disorders that affect people of all ages, including children and adults. The goal of this study was to look into the relationship between genetic alterations and the two diseases mentioned above as well as to develop a PPI network or related channel. The first step is to determine whether or not there is a biological relationship between them. This would assist us in connecting both of them as well as building therapeutic drugs that are effective against stress and depression disorders. Using R programs, the genes that are responsible for different diseases are acquired, pre-processed, analyzed, and mined in order to better understand them. During the study, a novel pathway was discovered. Based on common genes between the two diseases studied, the PPI network, gene-miRNA interaction, TF-gene interaction, and PDI network were established. This data can help us better understand how the PPI network binds to its ligands. We anticipate that our study will contribute to the development of new drugs for stress and depression.
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    Power Quality Disturbance Classification Using Frequency Domain Features
    (DEPARTMENT OF ELECTRICAL, ELECTRONIC AND COMMUNICATION ENGINEERING (EECE), MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY, DHAKA, BANGLADESH, 2013-12) Ibne Yousuf, Md. Adib; Shamim, S.M.; Rahman Akanda, Md. Touhidur
    Discrete Cosine Transform feature has become an effective feature extraction method in modern power system. In this paper, we used algorithm of a unique feature for power quality (PQ) disturbance signal classification. Here, we proposed the extraction of spectral features from Discrete Cosine Transform (DCT) domain. This feature extraction offers the ability to detect and localize harmonic events and it also classifies different power quality disturbance signals. A useful technique of selecting significant DCT coefficients is proposed for optimal feature selection. This process offers dimensional feature reduction. In this paper we consider seven types of power quality disturbance signals and simulate for each of the given categories. Using this extracted feature we can get not only very high classification accuracy but also a low computational burden. This feature extraction using Discrete Cosine Transform is one of the best feature extraction formula, we have ever seen.
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    Power Quality Disturbance Classification Using Frequency Domain Features A
    (Department of Electrical Electronic and Communication Engineering Military Institute of Science and Technology, 2013-12) Yousuf, Md. Adib Ibne; Shamim, S.M.; Akanda, Md. Touhidur Rahman
    Discrete Cosine Transform feature has become an effective feature extraction method in modern power system. In this paper, we used algorithm of a unique feature for power quality (PQ) disturbance signal classification. Here, we proposed the extraction of spectral features from Discrete Cosine Transform (DCT) domain. This feature extraction offers the ability to detect and localize harmonic events and it also classifies different power quality disturbance signals. A useful technique of selecting significant DCT coefficients is proposed for optimal feature selection. This process offers dimensional feature reduction. In this paper we consider seven types of power quality disturbance signals and simulate for each of the given categories. Using this extracted feature we can get not only very high classification accuracy but also a low computational burden. This feature extraction using Discrete Cosine Transform is one of the best feature extraction formula, we have ever seen