Computer Integrated Manufacturing Systems <p align="justify">"<em><strong>Computer Integrated Manufacturing Systems</strong></em>" was founded in 1995 , Chinese monthly, English name Computer Integrated Manufacturing Systems , journal number <strong>ISSN: 1006-5911</strong> .</p> <p align="justify">This journal is a national academic journal founded by the national 863 high-tech research and development program CIMS and sponsored by the No. 210 Research Institute of China North Industries Group Corporation . When the publication was launched, Vice Premier Zou Jiahua and Song Jian, then director of the State Science and Technology Commission, wrote inscriptions to congratulate. The director of the first editorial board is the chief scientist in the field of automation, Academician Jiang Xinsong of the Chinese Academy of Engineering. The current honorary director of the journal editorial board is Academician Wu Cheng of the Chinese Academy of Engineering, and the director is Academician Li Peigen of the Chinese Academy of Engineering. There are 18 advisory editorial members and 77 editorial members. All are well-known experts in the field or experts with outstanding contributions . Among them, there are 15 academicians and 6 foreign experts.</p> <p align="justify"> The purpose of this journal is to exchange information on the research, development and application of advanced manufacturing technology at home and abroad, and to promote and promote the development of advanced manufacturing in China. It mainly reports the policy measures, key points, trends, scientific and technological achievements, scientific research trends, popularization and application, product development and academic activities related to the development of computer integrated manufacturing systems at home and abroad. This journal currently has , but is not limited to, the following columns: digital /networked/intelligent manufacturing technology, modern manufacturing service technology, product design and development technology, enterprise management and logistics technology , etc.</p> <p align="justify">Since its establishment more than 20 years ago , this journal has always been responsible for tracking the development trend of foreign advanced manufacturing technology, publicizing the latest research results obtained in this field in China, and reflecting the innovation of technology application. It has gathered a large number of top-level experts, scholars and engineering technicians in the field of advanced manufacturing technology in China, and has made important contributions to promoting the research, development and application of advanced manufacturing technology in China.</p> <p align="justify">The journal <em><strong>"Computer Integrated Manufacturing System"</strong></em> focuses on forward-looking, innovative, systematic and complete academically, and focuses on guidance and practicality in application, realizing the combination of academic research and engineering application. The papers published in this journal have high academic value and great influence, and fully reflect the latest research level, application achievements and valuable experience in the field of advanced manufacturing technology . .</p> <p align="justify">The journal currently has more than 700 senior reviewers in the field , of which more than 70% are experts with the title of professor or above . Most of the authors are researchers and organizers with doctoral degrees in high-level and powerful research and development units in China, and the proportion of dissertation funding exceeds 90%.</p> <p align="justify">The main readers of this journal are teachers and students from colleges and universities engaged in the research and application of advanced manufacturing technology, and researchers from scientific research institutes and enterprises.</p> <p align="justify">In 1996, the journal entered the core database of the US "Engineering Index ( Ei ) Compendex ", and the selection rate of papers has been very high, and has always maintained 100% in recent years. At the same time, this journal is also the source journal of many well-known databases at home and abroad, including Russian "Abstract Journal" ( AJ ), British "Scientific Abstracts" ( SA ), American "Cambridge Scientific Abstracts" ( CSA ), Poland "Copernicus Index" ( IC ), Netherlands "Digest and Citation Database" ( Scopus ) , American EBSCO database, "Chinese Academic Journal Impact Factor Annual Report", " WJCI Science and Technology Journal World Influence Index Report", "China Science and Technology Journal Citation Report" ( CJCR ) , "Overview of Chinese Core Journals 2017 Edition", " CCF Catalogue of High- Quality Journals in Computing Field", " CCF Recommended Catalogue of Chinese Sci-tech Journals", China Academic Journal Comprehensive Evaluation Database ( CAJCED)), China Science Citation Database ( CSCD ), RCCSE China Authoritative Academic Journals, Chinese Science and Technology Periodical Database, Chinese Electronic Periodical Service Database ( CEPS ), "China Academic Journal Abstracts (Chinese version)", "China Academic Journal Abstracts (English version)" )", Wanfang Digital Periodical Group, Chaoxing Periodical Domain Publishing Platform, VIP Database and other famous databases at home and abroad. And won the 3rd National Journal Award for 100 key journals, the first prize of China's weapons industry outstanding scientific and technological journals, as well as China's most internationally influential academic journals and China's international influential academic journals and other awards.</p> en-US (Editorial ) (Ex-Editor) Mon, 11 Sep 2023 00:00:00 +0000 OJS 60 ACOUSTIC FEEDBACK CANCELLATION FOR DIGITAL HEARING AIDS USING SIMPLIFIED MUTLIBAND-STRUCTURED KALMAN FILTER <p>Acoustic coupling between the microphone and the loudspeaker is a major issue in open-fit digital hearing aids. When compared to a close-fit hearing aid, an open-fit dramatically reduces signal quality and limits the potential maximum stable gain. Adaptive feedback cancellation (AFC) is a practical method for reducing the influence of acoustic coupling. However, because to the high correlation between the loudspeaker signal and the incoming signal, it might induce bias in calculating the feedback path if not carefully considered, especially when the incoming signal is spectrally coloured, as in speech and music. For decreasing this bias, the prediction error method (PEM) is well recognized. In this paper, we proposed a simplified multi-structure Kalman filter for implementing PEM based AFC. Kalman filter allows further increase in convergence/tracking rates and the high computational complexity of generalized Kalman filter is reduced by multi-structured topology and this in turn reduce the computational complexity and also subband topology provide low processing delay. To overcome the reconvergence inability of during the change in feedback path, switched combination of SMKF and NLMS is used. Simulation results showed that the proposed algorithm performed better</p> S.Siva Prasad* & CB Rama Rao (Author) Copyright (c) 2023 Mon, 11 Sep 2023 00:00:00 +0000 A MACHINE LEARNING APPROACH FOR EARLY RENAL RISK PREDICTION USING IMPROVED SSA AND ANNGO <p>Chronic kidney disease (CKD) is a general term encompassing a number of diverse kidney diseases with increasing frequency and prevalence as well as harmful side effects includes renal failure, cardiovascular disease, and early death. It is sometimes referred to as Chronic Renal Disease a stage of advanced renal function loss. Unfortunately, there is no cure for this ailment, but it is possible to halt its growth and mitigate the harm by diagnosing it early. As a result, it is now of utmost importance to identify such illnesses at an early stage. If the risk factors for chronic kidney disease are identified early on, timely treatment and the proper course of action may be performed. Along with mining tools, machine learning is crucial in overcoming this obstacle. In this research, we used a hybrid model and feature selection technique to construct an hybrid machine learning model to forecast CKD. With an accuracy of 99.87%, the results demonstrated that the proposed classifier performed best in the renal diagnosis method.</p> K. Saranyadevi* & Dr. P. Rathiga (Author) Copyright (c) 2023 Fri, 15 Sep 2023 00:00:00 +0000