Firas Abedi¹, Hayder M. A. Ghanimi², Abeer D. Algarni³, Naglaa F. Soliman^3,*, Walid El-Shafai^4,5, Ali Hashim Abbas⁶, Zahraa H. Kareem⁷, Hussein Muhi Hariz⁸, Ahmed Alkhayyat⁹

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2791-2814, 2023, DOI:10.32604/csse.2023.038762

Abstract Data mining plays a crucial role in extracting meaningful knowledge from large-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp optimization algorithm-based feature selection (COAFS)… More >

V. Banupriya^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5597-5613, 2023, DOI:10.32604/cmc.2023.038696

Abstract In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly,… More >

Karthick Panneerselvam^1,2,*, K. Mahesh¹, V. L. Helen Josephine³, A. Ranjith Kumar²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1047-1061, 2023, DOI:10.32604/csse.2023.030513

Abstract Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using… More >

R. Raja^1,*, N. Satheesh², J. Britto Dennis³, C. Raghavendra⁴

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2857-2874, 2023, DOI:10.32604/iasc.2023.026153

Abstract In IoT, routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance. The evaluation of optimal routing and related routing parameters over the deployed network environment is challenging. This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory (s − LSTM) and Bi-directional Long Short Term Memory (b − LSTM). It is used to hold the routing information and random routing to attain superior performance. The proposed model is trained based on the searching and detection mechanisms to compute the packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, etc. The anticipated… More >