Journal of Autonomous Intelligence最新文献

{"title":"A meta-study on optimizing healthcare performance with artificial intelligence and machine learning","authors":"B. Lainjo","doi":"10.32629/jai.v7i5.1535","DOIUrl":"https://doi.org/10.32629/jai.v7i5.1535","url":null,"abstract":"This study explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, focusing on enhancing patient care through operational efficiency and medical innovation. Employing a meta-study approach, it comprehensively analyzes the applications and ethical aspects of AI and ML in healthcare, highlighting successful implementations like IBM Watson for Oncology and Google DeepMind’s AlphaFold. The research emphasizes AI’s significant contributions to diagnostics, precision medicine, and medical imaging interpretation, alongside its role in optimizing healthcare operations and enabling personalized medicine through data analysis. However, it also addresses challenges such as algorithmic bias, safety, data privacy, and the need for regulatory frameworks. The study underlines the importance of continued research, interdisciplinary collaboration, and adaptive regulations to ensure the responsible and ethical use of AI and ML in healthcare.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"40 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140258484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The implications of Artificial Intelligence on international development management","authors":"B. Lainjo","doi":"10.32629/jai.v7i5.1258","DOIUrl":"https://doi.org/10.32629/jai.v7i5.1258","url":null,"abstract":"Artificial Intelligence (AI) has emerged as a powerful tool revolutionizing various sectors globally, including international development management. This research aims to explore the current landscape of AI implementation in global development management, assess the benefits and challenges associated with its adoption, and propose relevant policies and practices. A mixed research design, comprising qualitative and quantitative methods, was utilized to gather data from secondary sources. The qualitative section of the study draws upon case studies from diverse operational sectors to examine the impact of AI adoption. These case studies highlight how AI contributes to improved performance in various industries and the potential positive effects on individuals’ lives. The quantitative part of the research utilizes data from renowned databases such as World Bank Open Data, United Nations Development Programme, International Monetary Fund (IMF), OECD Stat, and Global Open Data Index. Integrating qualitative and quantitative data allows for a comprehensive understanding of AI implementation’s economic growth and development across different organizations worldwide. The findings reveal that AI adoption in international development management holds significant promise for enhancing organizational efficiency and individuals’ well-being. However, the research also identifies various challenges associated with AI implementation, such as ethical considerations and potential job displacement. To address these issues, the study proposes policy recommendations and best practices that can guide organizations and policymakers in effectively harnessing the transformative potential of AI. This research contributes to international development management by providing a deep understanding of the importance of AI in the current context. The study offers insights for organizations adopting AI and assists policymakers in identifying and resolving pertinent challenges. By completing this study, organizations and policymakers can proactively address the existing problems and develop strategies to maximize the benefits of AI while minimizing potential risks. In summary, this research underscores the immense potential of AI in driving development and improving lives, laying a foundation for future advancements in international development management.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"39 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Management control tools for Moroccan industrial companies: Application of Target Costing and Artificial Intelligence","authors":"Salim Merjane, Chaimaa Touili, Mouaad Khalil, Karima Touili, Mohammed Fikri","doi":"10.32629/jai.v7i5.1524","DOIUrl":"https://doi.org/10.32629/jai.v7i5.1524","url":null,"abstract":"Technological advances and intensified competition on world markets have led to product diversification, forcing companies to adjust their production and marketing strategies using modern management tools. However, to meet the complex challenges posed by cost management and production process optimization, an essential dimension to consider is the integration of artificial intelligence into the Target Costing process. The main objective of this article is to detail the implementation of this Japanese method in the Moroccan context, focusing in particular on Moroccan industrial companies. To achieve our goal, an extensive literature review has been undertaken. However, there is a lack of documentation concerning the understanding of this method and its impact on Moroccan industrial companies. In order to better understand this relationship, we undertook a literature review focusing on the role of this instrument within industrial companies in Morocco, adopting an analytical approach. It should be noted that Target Costing is a mechanism for adapting companies to the Moroccan environment, encouraging organizational change in response to the needs of the Moroccan market while ensuring its sustainability. It also fosters collaboration between stakeholders, as well as compliance with Moroccan regulations and standards, helping to preserve the competitive edge of Moroccan industrial companies. In the Moroccan context, the adoption of artificial intelligence as part of Target Costing could bring significant benefits to industrial companies. These include a better understanding of local market needs, optimization of production processes to meet changing demands, and more informed decision-making when defining product selling prices. This study presents contributions from both academic and managerial perspectives, although it does raise certain limitations linked to the scarcity of articles and publications in the Moroccan context.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"7 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A blockchain-based deep learning approach for cyber security in next-generation medical cyber-physical systems","authors":"B. Balogun, Khushboo Tripathi, Shrikant Tiwari, Shyam Mohan J S, Amit Kumar Tyagi","doi":"10.32629/jai.v7i5.1478","DOIUrl":"https://doi.org/10.32629/jai.v7i5.1478","url":null,"abstract":"Cyber-physical systems (CPSs) have been employed to seamlessly integrate numerous processes and physical components with integrated computing facilities and data storage, aiming to achieve a heightened level of effectiveness and efficiency across various qualitative and quantitative metrics, including technical and organizational aspects. The increased use of the web and the prospering network through IoT (Internet of things) have given a critical open door to CPS to prevail. While this innovation is as of now utilized in programmed pilot flying, advanced mechanics frameworks, clinical checking, modern control frameworks, and so forth, the headway of these frameworks should understand undeniable spotlight on making them proficient and secure. To work on the strength, reliability, and security of these frameworks, specialists can integrate blockchain innovation which has an inbuilt mix of consensual calculations, secure conventions, and circulated information capacity, with the CPS. This introduces an efficient deep learning approach based on blockchain for medical cyber-physical systems (CPS), consisting primarily of two components: a) a blockchain based security framework to protect the medical data and b) the extraction of quintessential features from these data to a classifier for performing the anomaly scans using deep learning. The experimental evaluation demonstrates that the suggested system outperforms existing models, achieving exceptional performance with an accuracy rate of 0.96 and a sensitivity score of 0.95.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"13 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VR based gesture elicitation for user—Interfaces with low vision","authors":"K. Kalaiselvi, R. Bhuvaneswari, R. Rekha, T. R. Kumar, Rishav Banerjee, Omang Baheti","doi":"10.32629/jai.v7i5.1056","DOIUrl":"https://doi.org/10.32629/jai.v7i5.1056","url":null,"abstract":"User interfaces (UI) and menus in virtual reality (VR), which frequently replicate traditional UI for computers and smartphones, are not created factoring for individuals with low eyesight as they demand accurate pointing and good vision to engage effectively. As an alternative method of user interaction with UI, using gestures can be recommended. Comparing gesture-based interaction with the conventional point-and-click technique for changing system settings like volume, brightness, and window manipulation in order to test this hypothesis is employed. Accessibility, spatial awareness, and precision for those with low vision while lowering cognitive load and enhancing immersion for all users can be improved by leveraging gestures. The objective of the research work is to explore the framework of Gesture Elicitation in VR environments for users with low vision. In this research work the usage of gestures as a more effective and immersive means of interacting with menus, which will not only enhance the experience of normal VR users but also drastically reduce the friction experienced by those with visual impairments is proposed. User studies demonstrate a noticeable improvement in the aforementioned areas, with faster work completion times, more immersion, and better user satisfaction.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"63 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140264662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualization for a new era: Impact and application of large language models and AIGC to traditional business models","authors":"Qianqian Yang, Ngai Cheong, Dejiang Wang, Shi Li, Oi Neng Lei","doi":"10.32629/jai.v7i4.1487","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1487","url":null,"abstract":"This paper focuses on the application and business value of large-scale language models, such as GPT and Ernie’s model. These models combined with AIGC tools like stable diffusion generate images with fixed styles, character traits, and continuous plots using randomized story scripts. As a result, it enhances the operational efficiency between or within industries widely, and it fully demonstrate their business value. On the technical side, this paper describes in detail of building a pipeline to generate cue words required for stable diffusion, in which using large-scale language models and story scripts. Subsequently, the limitations of text-to-image are summarized by comparing the traditional method and language model, i.e. comparing characteristics from traditional book production and images generated using language model’s cue words. This leads to a supervised multiround iterative LoRA modeling scheme that utilizes CLIP to achieve character IP fixation. To evaluate the impact of the application direction, we combine application scenarios and researches on application aspects regarding current AIGC industry structure, we found that the AIGC tool has several major aspects, mainly includes the aspects of basic big model, industry and scenario models, business and domain small models, AI infrastructure and AIGC supporting services. big model and AIGC techniques generate images with no specific rules and have less limitation. We call this ‘visualization’ in the new AI era. In this paper, we explore the possible impacts and economic values when changing from traditional domain to the new AI ear.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"160 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transportation logistics monitoring for transportation systems using the machine learning","authors":"Manmohan Singh Yadav, Rupesh Shukla, C. Parthasarathy, Divya Chikati, Radha Raman Chandan, Kapil Kumar Gupta, Shashi Kant Gupta","doi":"10.32629/jai.v7i4.1321","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1321","url":null,"abstract":"To decrease the number of accidents, Transportation Systems (TS) work to increase traffic efficiency and vehicular flow in urban areas. The production of datasets to carry out an in-depth analysis of the data using machine learning techniques is made possible by the generation of huge volumes of data generated by all the digital devices connected to the transportation network. This paper proposed a machine learning technique called Gradient Descent K-Nearest Neighbors (GD-KNN) for transportation logistics monitoring to improve route optimization, demand forecasting, vehicle maintenance, real-time monitoring, freight optimization, risk assessment, and continuous improvement. By harnessing data from various sources such as GPS devices, sensors, telemetric, and historical transportation data, machine learning algorithms can analyze and process this data to make accurate predictions and recommendations. The collected dataset was pre-processed using z-score normalization, and then Independent Component Analysis (ICA) was applied for the feature extraction process. Real-time monitoring enables the detection of anomalies and delays, providing alerts for timely actions. Freight optimization is achieved by analyzing parameters like weight, size, and delivery locations, resulting in cost reduction and improved load balancing. GD-KNN assesses risks and security threats using data from security systems, ensuring the safety of goods and personnel. Continuous learning allows the system to adapt to changing conditions and improve predictions over time. Overall, GD-KNN empowers transportation logistics monitoring to optimize operations, enhance customer service, and reduce costs in transportation systems.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Word translation for Indo-Aryan languages using different retrieval techniques","authors":"Kiranjeet Kaur, S. Chauhan","doi":"10.32629/jai.v7i4.1455","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1455","url":null,"abstract":"The study of Natural Language Processing has been revolutionized by word embedding, enabling advanced language models to understand and generate human-like text. In this research article, we delve deep into the world of word embedding, aiming to provide a comprehensive exploration of its underlying principles, methodologies, and applications. One important factor that affects many multilingual language processing activities is the word translation or incorporation of bilingual dictionaries. We used bilingual dictionaries or parallel data for translation from one language to another. For this research work, this problem is addressed, and also generating the best cross-lingual word embedding for the different language pairs. So, we are using an aligned document sentence-aligned corpus, or any bilingual dictionary for this research analysis. For the most frequent word, we are assuming that there is an intra-lingual similarity distribution, and both the source and the target corpora have a comparable distribution graph. Additionally, these embeddings are isometric. These cross-lingual word embeddings are used for cross-lingual transfer learning and unsupervised neural machine translation. This research aims to improve the accuracy and efficiency of word translation between different language pairs by employing different retrieval techniques. The study analyzes the effectiveness of these techniques on different language pairs, including English-Hindi, English-Punjabi, English-Gujarati, English-Bengali, and English-Marathi. The research is expected to contribute significantly to the field of language translation by introducing innovative methods and other applications.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"66 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An insight on the interventions of AI in healthcare—A bibliometric study","authors":"Vineed Kumar Vijayan, Smiju Is, Jose John","doi":"10.32629/jai.v7i4.1148","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1148","url":null,"abstract":"Background: The literature on artificial intelligence (AI) in healthcare is expanding quickly and is a key factor in healthcare promotion. Objective: This analysis’s goal is to offer a dynamic and comprehensive bibliometric analysis of publications on artificial intelligence in the field of health care. Methods: All currently available and highly referenced healthcare-related AI research papers published in English up to April 2023 were found by searching the Web of Science (Clarivate PLC). A search technique was created based on bibliometric indications to evaluate the title’s eligibility, using the abstract and full text as necessary. Results: 6254 items were found during the search, and 3107 of those papers were used in the analysis. USA was the country that published most research papers in the field of AI in healthcare. India stood in 4th place, with China and the United Kingdom in front of them. Relevant Affiliations were found in Stanford University, Harvard Med School, followed by King Abdul Aziz University. Conclusion: Future research should concentrate on bridging the gaps between clinical applications and AI healthcare research. More research should be done, especially in the areas of ethics, data governance, clarity of data, and additional inputs in the form of training that might be required for healthcare workers to update their skills in the world of AI-assisted healthcare.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"180 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing gastric cancer diagnosis through advanced machine learning approaches","authors":"Danish Jamil, S. Palaniappan, Muhammad Numan Ali Khan, Syed Mehr Ali Shah","doi":"10.32629/jai.v7i4.1021","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1021","url":null,"abstract":"Early detection of gastric cancer through a Computer-Aided Detection (CAD) system has the potential to significantly reduce the mortality rate associated with this disease. This study aims to investigate the effects of class imbalance on the performance of machine learning classifiers in this context. Using a dataset of 145,787 screening records from NHS Liverpool Hospital, we employed stratified sampling to create balanced and unbalanced datasets and evaluated the performance of four machine learning algorithms—Logistic Regression, Support Vector Machine, Naive Bayes, and Multilayer Perceptron—under five different test conditions. The study’s novelty lies in its detailed examination of class imbalance in gastric cancer diagnosis, emphasizing the crucial role of balanced datasets in machine learning-based early detection systems. For the MLP model under 10-fold cross-validation, the Class 0 sensitivity (non-cancer cases) of the unbalanced dataset was 0.968, higher than the balanced dataset’s 0.902. However, the Class 1 sensitivity (cancer cases) and Positive Predictive Value (PPV) of the unbalanced dataset were much lower (0.383 and 0.527) than those of the balanced dataset (0.959 and 0.907), indicating a significant improvement in identifying true positive cases when using a balanced dataset. These findings highlight the negative effect of class imbalance on prediction accuracy for positive cancer cases and underscore the importance of addressing this imbalance for more reliable and accurate predictions in medical diagnosis and screening. This approach has the potential to improve patient outcomes and may contribute to strategies aimed at reducing the mortality rate associated with gastric cancer.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"76 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}