Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in comparison with existing SVM algorithms.
Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature
... Show MoreThe Vulnerable Indian Roofed Turtle Pangshura tecta (Gray, 1831) (Testudines: Geoemydidae) occurs in the Sub-Himalayan lowlands of India, Nepal, Bangladesh, and Pakistan. Little is known about its natural history, no studies have been conducted revealing its natural predators. In this study, a group of Large-billed Crow Corvus macrorhynchos Wagler, 1827 (Passeriformes: Corvidae) was observed hunting and predating on an Indian Roofed Turtle carcass in the bank of river Kuakhai, Bhubaneswar, India. The first record of this predation behaviour is reported and substantiated by photographic evidence.
This study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula
... Show MoreArtificial intelligence has quickly invaded the realms of both creative and information-based writing, raising new questions about human originality, authorship and style. Despite its ability to produce writings that are coherent and stylistically varied, there are still concerns over the uniqueness and cultural neutrality of AI programs such as ChatGPT. This review covers significant recent advancements with artificial intelligence applications in both the literary and non-literary fields. It analyzes 35 recent studies contrasting authorship and creativity, or stylistic considerations and impressions, between human and AI texts. These studies range from poetic and fictional writing through essay, news article and academic publicati
... Show MoreSoftware-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
... Show MoreBackground: To evaluate the bony supports of the teeth adjacent to the area of cleft in patient with unilateral cleft lip and palate and to compare these measurements with the measurements of the same teeth in non-cleft side by using CBCT. Materials and methods: The CBCT scans of 30 patients having cleft lip( unilateral) and palate(unilateral), were analyzed and the measurements of the alveolar bony support for teeth that are adjacent to the cleft area were measured with those teeth located on opposite side (non- clef) side. For each tooth, the measurements will taken for the distance between the( cementoenamel junction) (CEJ) and the bony crest (AC) at the( buccal area) was measured and the thickness of the buccal plate At zero, one, tw
... Show More