Application of Approximate Equality for Reduction of Feature Vector Dimension

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript An Image Visual Quality Assessment Method Based on SIFT Features Guangyi Chen, Stephane Coulombe JPRR Vol 8, No 1 (2013); doi:10.13176/11.511

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Pattern Similarity Score Based on One Dimensional Time Series Analysis Paawan Sharma, Mukul Kumar Gupta, Amit Kumar Mondal, Vivek Kaundal JPRR Vol 12, No 1 (2017); doi:10.13176/11.744

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript GrepVS - a Combined Approach for Graph Matching Diego Reforgiato Recupero JPRR Vol 4, No 1 (2009); doi:10.13176/11.85

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Bangla Basic Character Recognition Using Digital Curvelet Transform Majumdar Angshul JPRR Vol 2, No 1 (2007); doi:10.13176/11.27

The 2025 3rd International Conference on Computer, Machine Learning and Artificial Intelligence (CMLAI 2025) had been held in San Francisco, USA during March 29-30, 2025. CMLAI 2025 is an international forum devoted specifically to facilitate synergies in research and development in the areas of computer science, machine learning, neural networks, algorithm design, and artificial intelligence. It provides the communication platform for those who are leading experts and scholars from around the world. The development of artificial intelligence and their corresponding technologies and industries has also been regarded as "national development strategy" for the world's leading countries and gradually developed into a new "trump card" in the game between countries. This conference was attended by over 60 researchers, scholars, academic and industry experts from many countries. During the two-day conference, 4 keynote speeches with topics on cloud computing, machine learning, neural networks, and artificial intelligence were delivered. And 24 oral and invited presentations were presented and discussed in-depth in 4 technical sessions, with interactive communication among the attendees. The conference successfully promoted exchanging new ideas and perspective discourse among the participants in their respective fields. CMLAI Organizing Committees San Francisco, USA

The Journal of Pattern Recognition Research (JPRR) provides an open access forum for the publication of research articles in areas of pattern recognition, machine learning, artificial intelligence, computational algorithms, and fuzzy learning

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Artificial Intelligence applications in healthcare: A bibliometric and topic model-based analysis

Smart cities and villages have enhanced the quality of lives of residents. Various computer-assisted technologies have been harnessed for the development of smart cities and villages in order to provide solutions for common and niche urban problems. The development of smart environments has been possible due to advances in computing power and artificial intelligence (AI) that have allowed the deployment of scalable technologies. Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges summarizes the role of AI in planning and designing smart solutions for urban and rural environments. This book is divided into three sections to impart a better understanding of the topics to readers. These sections are: 1) Demystifying smart cities and villages: A traditional perspective, 2) Smart innovations for rural lifestyle management solutions, and 3) Case studies. Through this book, readers will be able to understand various advanced technologies that are vital to the development of smart cities and villages. The book presents 15 chapters that present effective solutions to urban and rural challenges. Concepts highlighted in chapters include smart farms, indoor object classification systems, smart transportation, blockchains for medical information, humanoid robots for rural education, IoT devices for farming, and much more. This book is intended for undergraduate and graduate engineering students across all disciplines, security providers in the IT and related fields, and trainees working for infrastructure management companies. Researchers and consultants at all levels working in the areas of artificial intelligence, machine learning, IoT, blockchain, network security, and cloud computing will also find the contents beneficial for planning projects involving smart environments.

The prediction of Arabic handwritten characters is one of the fascinating matters in the area of artificial intelligence (AI) and machine learning, especially in the case of on-line handwriting. In the handwriting recognition system, characters are classified according to their particular categories by utilizing -as input- the values of the features extracted from them. Our major objective in this paper is to classify the isolated characters for online Arabic handwriting (? to ?) using machine learning techniques with the features from EDMs (Edge Direction Matrixes) as opposed to EEDMs (Extended Edge Direction Matrixes). With respect to all classification methods used in this study, EEDMs with machine learning techniques had better performance than EDMs in recognizing isolated Arabic online characters.

Continual learning in medical devices: FDA's action plan and beyond

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

An Enquiry on Similarities between Renormalization Group and Auto-Encoders Using Transfer Learning

Introduction: The paper explores the trends in the field of artificial intelligence, especially analyzing the improvements in deep learning and machine learning that are revolutionizing the computer science discipline. The study gives details of the current trends and new approaches, possible uses, and other possible advancements. The author discusses a literature review incorporating trends identified in the prior year to discuss what these trends mean for academia and industry as well as the future of AI in computer science. Artificial intelligence has developed quite fast to be one of the key factors in the progress of computer science. Out of all the subfields of AI, DL and ML are receiving substantial attention from academicians as well as industrialists owing to the game-changing effects that these two fields have brought in data processing, decision-making, and automation zones. These technologies have not only helped transform conventional solution-seeking procedures in computer science but have also facilitated various new courses for research. Methodology: This work employs the literature review and analysis from 2015 to 2024 approach for the analysis of current advancements and emerging trends in deep learning and machine learning in computer science. The analysis of the research papers published in recent years, the industry reports, and case studies in the paper presents the views seeking to understand the state of AI in computer science. The examination is built based on categories, which are algorithmic improvement, real-world uses, and artificial intelligence implementation across industries. This approach means a systematic assessment of how deep learning and machine learning affect the field for better. Conclusion: An analysis of the trends in inventiveness of AI shows that the future of AI, especially in areas such as deep learning as well as machine learning, indicates increased inventiveness at a very impressive pace. It is one thing to use these technologies to progress computer sciences to the limits of what is possible today. It is clearly seen that the future of AI heavily relies on the future advancement of deep learning and machine learning, where it is anticipated to increase in its importance within artificial intelligence. The findings of this paper affirm the necessity to advance the studies of the specific areas of artificial intelligence proposed and call for interdisciplinarity to harness the potential of artificial intelligence for computer science.

In today’s world, machine learning has gained much popularity, and its algorithms are employed in every field such as pattern recognition, object detection, text interpretation and different research areas. Machine learning, a part of AI (artificial intelligence), is used in the designing of algorithms based on the recent trends of data. This paper aims at introducing the algorithms of machine learning, its principles and highlighting the advantages and disadvantages in this field. It also focuses on the advancements that have been carried out so that the current researchers can be benefitted out of it. Based on artificial intelligence, many techniques have been developed such as perceptron-based techniques and logic-based techniques and also in statistics, instance-based techniques and Bayesian networks. So, overall this paper produces the work done by the authors in the area of machine learning and its applications and to draw attention towards the scholars who are working in this field.