Journal of Autonomous Intelligence最新文献_第9页

Data analytics for finding emerging entrepreneur’s success factors 通过数据分析发现新兴企业家的成功因素

Journal of Autonomous Intelligence Pub Date : 2023-12-13 DOI: 10.32629/jai.v7i2.956

Nitin Shekapure, Dipti D. Patil, S. Shekapure

引用次数: 0

Futuristic business intelligence framework for start-ups 面向初创企业的未来型商业智能框架

Journal of Autonomous Intelligence Pub Date : 2023-12-13 DOI: 10.32629/jai.v7i2.960

Deepika Ajalkar, Shaik Abdul Waheed, Mohammed Abdul Matheen, Poonam Gupta, Jayashri Prashant Shinde

{"title":"Futuristic business intelligence framework for start-ups","authors":"Deepika Ajalkar, Shaik Abdul Waheed, Mohammed Abdul Matheen, Poonam Gupta, Jayashri Prashant Shinde","doi":"10.32629/jai.v7i2.960","DOIUrl":"https://doi.org/10.32629/jai.v7i2.960","url":null,"abstract":"Small businesses like start-ups and freelancing are on the rise these days. Economic changes implemented in India provided a watershed moment for India’s diverse sectors, as well as for Asia’s start-up ecosystem. However, these start-ups face a lack of financial preparation, and as a result, some of them fail. The current article covers the notion of assisting newcomers in the market with financial planning. This article intends to equip new start-ups with methods and tools such as investment plans, social marketing planning, finance management, work-life balance, savings plans, and future finance plans. Some extras include taxes storage facilities. Start-ups and freelancers use several platforms for different objectives, therefore they face many issues in communications, time planning. This article also seeks to remove the aforementioned concerns by offering a single platform for the organization’s chat, calendar, to-do lists, announcements, alerts, and Payrolls, attendance, and so on. Furthermore, for business enhancement, the project strives for business analytics as the future scope and will give a single platform for client needs to decrease mistakes to a minimum. This article introduces the “Xenom” framework, which is used to realise the optimisation design of the business management system and information analysis platform. Furthermore, the collaboration of diverse platforms would eliminate time gaps, allowing businesses to follow their aims and forecast more efficiently.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"128 S196","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006306","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}

引用次数: 0

Knowledge interaction analysis of cooperative digital green innovation of photovoltaic building materials enterprises based on reciprocity theory 基于互惠理论的光伏建材企业合作式数字化绿色创新的知识互动分析

Journal of Autonomous Intelligence Pub Date : 2023-12-13 DOI: 10.32629/jai.v7i2.1044

Yueyue Song, Yingying Zhang, Yudan Zhao, Shi Yin, Chen-Ming Hu

{"title":"Knowledge interaction analysis of cooperative digital green innovation of photovoltaic building materials enterprises based on reciprocity theory","authors":"Yueyue Song, Yingying Zhang, Yudan Zhao, Shi Yin, Chen-Ming Hu","doi":"10.32629/jai.v7i2.1044","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1044","url":null,"abstract":"In the process of collaborative digital green innovation of photovoltaic building materials enterprises, knowledge sharing between photovoltaic building materials enterprises and academic and research institutions is conducive to the achievement of win-win goals of enterprises and academic and research institutions. However, due to the non-contractual relationship between cooperative subjects, it is difficult to observe the efforts of members, which is easy to cause poor information. Therefore, knowledge reciprocity incentive is particularly important. In this paper, the sequential reciprocity model is introduced to analyze the knowledge interaction between photovoltaic building materials enterprises, and academic and research institutions on cooperative green innovation. The results show that: (1) when the reciprocity sensitivity of academic and research institutions is large enough, academic and research institutions can feel the goodwill conveyed by the high effort level of knowledge sharing, and will reciprocate with friendly behavior. (2) When the reciprocity sensitivity of academic and research institutions is small, they will choose to pay a low level of effort in knowledge sharing. (3) When the reciprocity sensitivity of academic and research institutions is in the middle value, the higher effort level of the institutions will increase with the increase of reciprocity sensitivity of the institutions. In this paper, the sequential reciprocity model is introduced to study the reciprocity incentive effect of knowledge sharing in enterprise cooperative digital green innovation from the perspective of dynamic domain, in order to enrich the reciprocity theory and provide reference for the knowledge sharing incentive problem of enterprise cooperative digital green innovation.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"109 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139003698","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}

引用次数: 0

Enhancing data security of cardiac patients in IoMT with Twin-Shield Encryption 利用双盾加密技术加强物联网医疗中心脏病患者的数据安全

Journal of Autonomous Intelligence Pub Date : 2023-12-13 DOI: 10.32629/jai.v7i2.1322

Smiley Gandhi, T. Poongodi, K. S. Kumar

{"title":"Enhancing data security of cardiac patients in IoMT with Twin-Shield Encryption","authors":"Smiley Gandhi, T. Poongodi, K. S. Kumar","doi":"10.32629/jai.v7i2.1322","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1322","url":null,"abstract":"Cardiac disease kills most people worldwide. Predicting and monitoring cardiac problems early improves disease treatment and patient outcomes. The Internet of Medical Things (IoMT) can monitor and analyze physiological data in real-time, changing healthcare. Many researchers find data generation problematic. Encryption is needed to secure a massive amount of data. This paper presents a Twin-Shield Encryption (TSE) that combines Elliptic Curve Cryptography (HECC) and Rivest-Shamir-Adleman (RSA) IoMT assistance for heart illness patient monitoring. Cleveland cardiac dataset from the University of California Irvine (UCI) research repository is collected. It has 12 qualities and 303 occurrences. The data is pre-processed using normalization; feature extracted using Principal Component Analysis (PCA), and securely transmitted to the cloud infrastructure for further processing and analysis. TSE encrypts patient data to prevent unauthorized access and maintain data integrity during transmission and storage. The framework could enhance cardiac ailment diagnosis, treatment, and management by giving clinicians and patients individualized care based on physiological profiles.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139005239","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}

引用次数: 0

Bayesian and non-Bayesian analysis for the lifetime performance index based on generalized order statistics from Pareto distribution 基于帕累托分布的广义阶次统计的寿命性能指标贝叶斯和非贝叶斯分析

Journal of Autonomous Intelligence Pub Date : 2023-12-13 DOI: 10.32629/jai.v7i2.1017

Amal S. Hassan, E. Elsherpieny, Ahmed M. Felifel

引用次数: 0

Improvement of support vector machine for predicting diabetes mellitus with machine learning approach 用机器学习方法改进支持向量机预测糖尿病的能力

Journal of Autonomous Intelligence Pub Date : 2023-12-13 DOI: 10.32629/jai.v7i2.888

Christine Dewi, Jernius Zendrato, Henoch Juli Christanto

{"title":"Improvement of support vector machine for predicting diabetes mellitus with machine learning approach","authors":"Christine Dewi, Jernius Zendrato, Henoch Juli Christanto","doi":"10.32629/jai.v7i2.888","DOIUrl":"https://doi.org/10.32629/jai.v7i2.888","url":null,"abstract":"The prevalence of diabetes is currently increasing worldwide, including in Indonesia, due to the increasing levels of stress and lack of physical activity that led to obesity and related complications such as hypertension. However, only about 25% of diabetes patients are aware of their condition. Therefore, this study aims to find an algorithm that can help predict with better accuracy using the diabetes mellitus dataset obtained from Kaggle. To obtain information about the accuracy level of diabetes diagnosis, the data will be processed using two methods, namely support vector machine and naive bayes. To obtain the most accurate results, we optimize each variant and parameter of every algorithm used. The best method in this study was produced by the support vector machine method with a radial basis function (RBF) kernel, which achieved an accuracy level of 98.25%, superior to the naive bayes method which obtained the highest accuracy of only 77.25%. Additionally, this study also applied the proposed method using the diabetes mellitus dataset from LAB01 DAT263x taken from the Kaggle website. The results of the experiment indicate that the suggested model outperforms other methods in terms of performance, with a tendency for high accuracy generated in every experiment for all datasets.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"20 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004818","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}

引用次数: 0

Lost item identification model development using similarity prediction method with CNN ResNet algorithm 利用 CNN ResNet 算法的相似性预测方法开发丢失物品识别模型

Journal of Autonomous Intelligence Pub Date : 2023-12-13 DOI: 10.32629/jai.v7i2.1381

Jonathan Prawira, Theresia Ratih Dewi Saputri

{"title":"Lost item identification model development using similarity prediction method with CNN ResNet algorithm","authors":"Jonathan Prawira, Theresia Ratih Dewi Saputri","doi":"10.32629/jai.v7i2.1381","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1381","url":null,"abstract":"Background: Incidents of personal belongings being lost often occur due to our negligence as human beings or criminal acts such as theft. The methods used to address such situations are still manual and ineffective. The manual process of reporting lost items requires significant time and effort. Additionally, matching the information of lost items with the found ones becomes increasingly difficult, and finding the original owners can be time-consuming. Objectives and Methods: This research aims to develop an approach that aids the community in the management of lost items by incorporating a process of item identification. It proposes the creation of an iOS-based prototype model that implements image comparison and string matching. The ResNet-50 architecture extracts features from images, and the Euclidean Distance method measures similarity between these features. Natural language processing used for text pre-processing and employs the cosine similarity metric to assess textual similarity in item descriptions. Result and Conclusion: By combining Euclidean distance and cosine similarity values, the model predicts similar lost item reports. Image comparison provides an accuracy result of 29.96% correctness, while string matching with 97.92% correctness. Thorough testing and validation confirm the model’s success across different reports.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"36 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139005268","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}

引用次数: 0

Identification of meningioma tumor using recurrent neural networks 利用递归神经网络识别脑膜瘤肿瘤

Journal of Autonomous Intelligence Pub Date : 2023-12-12 DOI: 10.32629/jai.v7i2.653

D. Anand, Osamah Ibrahim Khalaf, G. Abdulsahib, G. R. Chandra

{"title":"Identification of meningioma tumor using recurrent neural networks","authors":"D. Anand, Osamah Ibrahim Khalaf, G. Abdulsahib, G. R. Chandra","doi":"10.32629/jai.v7i2.653","DOIUrl":"https://doi.org/10.32629/jai.v7i2.653","url":null,"abstract":"By the calculations of national center for biotechnology information from COVID 19 pandemic, number of meningioma tumor patients are increasing in world. Identifying the meningioma tumor and its position in brain is not easy task by using deep neural networking based medical imaging. But it is needed to identify meningioma tumors in brain by using AI based medical imaging for the purpose of medical artificial intelligence technology innovation. Comparing to neural network results with recurrent neural network results can give accurate results. For identifying the patients’ present condition and prediction of future behavior by using recurrent neural network is need for us. Increase the accurate results for neural networking based medical imaging in health care is very expensive. By using recurrent neural networks (RNN) algorithm with many hidden layers for identification of tumor(s) in human brain with high accuracy by comparison of existing images in our data base with new unknown medical image with low cost. In this study first we are collecting the masks of skull from MRI image and dividing the masks to different types of datasets depending on age criteria like a child age, middle age and old age with two types male and female. Then we can get totally 6 types of datasets. All these masks of MRI images to binary imaging by using morphological erosion concept after that storing that masks in data sets then collect the new MRI image and comparing its mask part of skull with existing dataset in recurrent neural networks.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"15 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138977141","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}

引用次数: 0

Self-adaptive credit-based framework for blockchain-based IoT (BIoT) 基于区块链的物联网（BIoT）自适应信用框架

Journal of Autonomous Intelligence Pub Date : 2023-12-12 DOI: 10.32629/jai.v7i2.1183

Ritu Baniwal, Sunita Rani, Rashi Rastogi, Priyanka Priyanka, Anju Jain, Shashikant Madia

{"title":"Self-adaptive credit-based framework for blockchain-based IoT (BIoT)","authors":"Ritu Baniwal, Sunita Rani, Rashi Rastogi, Priyanka Priyanka, Anju Jain, Shashikant Madia","doi":"10.32629/jai.v7i2.1183","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1183","url":null,"abstract":"The Internet of Things (IoT) connects and improves crucial global technologies like sensor nodes. The Internet is evolving from a human-centric network to one that enables inanimate things to wirelessly communicate with one another. The lifespan of an IoT network may be affected by the energy requirements of its routing protocol. Data is transmitted through the internet, and it may compromise the security of the data. An attacker can access the data and modify the data in order to break the security of the network. Although various solutions are available, such as cryptography and steganography-based approaches, none provide secure data transmission in large-scale networks with low energy consumption. Blockchain technology plays a vital role in the prevention of network malware. In this paper, an attempt has been made to propose a credit-based mechanism for secure data transmission in an efficient manner with low energy consumption. In order to achieve optimal results, the proposed framework uses blockchain for data security and credit distribution to avoid delays. The proposed framework has been simulated using the Contiki Cooja (CC) simulator. The efficiency of the proposed framework is measured by comparing its performance with state-of-the-art techniques.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"24 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008999","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}

引用次数: 0

An integrated system for breast cancer diagnosis using convolution neural network and attention mechanism 利用卷积神经网络和注意力机制诊断乳腺癌的综合系统

Journal of Autonomous Intelligence Pub Date : 2023-12-12 DOI: 10.32629/jai.v7i2.943

Deepti Sharma, Rajneesh Kumar, Anurag Jian

{"title":"An integrated system for breast cancer diagnosis using convolution neural network and attention mechanism","authors":"Deepti Sharma, Rajneesh Kumar, Anurag Jian","doi":"10.32629/jai.v7i2.943","DOIUrl":"https://doi.org/10.32629/jai.v7i2.943","url":null,"abstract":"In most malignancies, breast cancer is fatal, accounting for approximately 500,000 annual deaths. The subtype of breast cancer known as Invasive Ductal Carcinoma (IDC) is surprisingly common. Pathologists commonly focus on IDC-containing regions when trying to determine if a patient has breast cancer. Although extremely fatal, survival rates and expected lifespans improve dramatically with prompt diagnosis and treatment. The treatment strategy also varies based on the breast cancer patient’s stage. In this research, we use a classification method for a publically available dataset of breast histopathology images obtained from the Kaggle. The IDC regions of the images in this dataset have been restricted for easy retrieval. The breast cancer IDC data set contains 277,524 records, of which 78,786 are positive. The 277,524 images were classified using an IDC breast cancer dataset, with 78,786 positive IDC and 198,738 negative IDC, respectively. The authors introduce a new architecture of deep convolutional neural networks and attention mechanism for classification. The model achieves state-of-the-art levels of accuracy for IDC identification, setting a new benchmark for future studies.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009392","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}

引用次数: 0