International Journal of Information Technology最新文献_第5页

A novel healthcare decision support system using IoT and ANFIS 使用物联网和 ANFIS 的新型医疗决策支持系统

International Journal of Information Technology Pub Date : 2024-08-31 DOI: 10.1007/s41870-024-02159-4

Naveen Kumar Dewangan, Neeti Pandey, Ritu Gautam, Avinash Krishna Goswami, Santosh Rameshwar Mitkari, Amanveer Singh, Anand Kopare, N. Gobi

{"title":"A novel healthcare decision support system using IoT and ANFIS","authors":"Naveen Kumar Dewangan, Neeti Pandey, Ritu Gautam, Avinash Krishna Goswami, Santosh Rameshwar Mitkari, Amanveer Singh, Anand Kopare, N. Gobi","doi":"10.1007/s41870-024-02159-4","DOIUrl":"https://doi.org/10.1007/s41870-024-02159-4","url":null,"abstract":"Modern healthcare facilities are equipped with major difficulties, particularly in poor nations where there are insufficient high-quality hospitals and medical professionals in remote places. Healthcare has profited from artificial intelligence’s revolution in many other areas of life. A few issues with the current architecture of the store-and-forward method of conventional telemedicine are that it requires a local health center with a dedicated staff, medical equipment to prepare patient reports, and long turnaround time for receiving a diagnosis and medication details from a medical expert in a main hospital, the cost of local health centers, and the requirement for a Wi-Fi connection. In this work, we present a new intelligent healthcare system built on cutting-edge technology such as deep learning and the Internet of Things (IoT). This system has the intelligence to use a medical decision support system to sense and process patient data by making use of adaptive neuro fuzzy inference system (ANFIS). For those who live in rural places, this system offers an affordable solution. By contacting local hospitals, users can determine whether they have a serious health concern and seek appropriate treatment. Additionally, the experiment findings demonstrate that the suggested system is capable of providing health services due to its efficiency and intelligence.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184740","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

ECG signal classification via ensemble learning: addressing intra and inter-patient variations 通过集合学习进行心电信号分类：应对患者内部和患者之间的差异

International Journal of Information Technology Pub Date : 2024-08-31 DOI: 10.1007/s41870-024-02086-4

Madhavi Mahajan, Sonali Kadam, Vinaya Kulkarni, Jotiram Gujar, Sanah Naik, Suruchi Bibikar, Ankita Ochani, Sakshi Pratap

{"title":"ECG signal classification via ensemble learning: addressing intra and inter-patient variations","authors":"Madhavi Mahajan, Sonali Kadam, Vinaya Kulkarni, Jotiram Gujar, Sanah Naik, Suruchi Bibikar, Ankita Ochani, Sakshi Pratap","doi":"10.1007/s41870-024-02086-4","DOIUrl":"https://doi.org/10.1007/s41870-024-02086-4","url":null,"abstract":"Electrocardiogram (ECG) signal classification is a cornerstone of automated heart abnormality detection. Unlike the limitations of human interpretation, AI techniques can effectively identify subtle patterns in ECG signals. This makes ECG a powerful non-invasive tool for assessing cardiovascular health. Existing methods for classifying ECG signals while valuable, they still struggle to achieve both high sensitivity and specificity. This limitation hinders their ability to deliver accurate and timely diagnoses for cardiac conditions. These shortcomings emphasize the need for more effective techniques to improve the precision of ECG signal classification. In response to these challenges, this study introduces a novel approach, using an ensemble methodology, a machine learning technique to enhance the precision of ECG classification through the fusion of signal and wave features. The proposed methodology addresses two key challenges: the transformation of paper ECG recordings into one-dimensional digital signals amenable to machine learning algorithms and the automated extraction of diagnostically significant features including the P wave, QRS complex, and T wave. Validation of the proposed methodology encompasses a comprehensive evaluation on a heterogeneous dataset comprising real-world and publicly available online resources. Noteworthy aspects of the evaluation include considerations of both intra-patient variations and inter-patient discrepancies, thus reflecting real-world complexities. Notably, in the realm of machine learning, the study employs ensemble algorithms and a soft voting classifier to enhance classification accuracy and robustness. This paper contributes to the advancement of automated ECG classification, offering a promising avenue for precise and reliable cardiovascular health assessment.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184737","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

Assessing digital transformation readiness: a comprehensive study of local clinics in Northwest Vietnam 评估数字化转型准备情况：对越南西北部地方诊所的综合研究

International Journal of Information Technology Pub Date : 2024-08-31 DOI: 10.1007/s41870-024-02182-5

An Hoai Duong, Thu Duc Nguyen, Giang Huong Duong, Thuy Thi Tran

{"title":"Assessing digital transformation readiness: a comprehensive study of local clinics in Northwest Vietnam","authors":"An Hoai Duong, Thu Duc Nguyen, Giang Huong Duong, Thuy Thi Tran","doi":"10.1007/s41870-024-02182-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02182-5","url":null,"abstract":"Local clinics are pivotal in delivering primary healthcare, especially in economically disadvantaged areas like Vietnam’s Northwest. However, these regions face notable deficits in healthcare infrastructure. Digital transformation offers a promising solution. This study assesses the digital transformation readiness of 75 local clinics in Northwest Vietnam and investigates the impact of influential factors on this readiness. The study design involved collecting responses from clinic heads or designated representatives through a web-based survey. The sample size comprised 75 local clinics in Northwest Vietnam. Multiple linear regressions were utilised to examine the impact of influential factors on the clinics’ digital transformation readiness. Findings indicate a significant readiness gap among the surveyed clinics, with observed scores falling below the maximum achievable score of 290. Most clinics scored between 63.5 and 116, highlighting substantial room for improvement in digital preparedness. The study unveiled significant relationships between digital readiness and clinic attributes. Negative correlations included clinic head age and reliance on e-wallets. Positive associations included seniority, social media engagement, and clinic characteristics like education and technology use. The regression results highlight positive associations with clinic head seniority, clinic social accounts, personnel using smart devices, and online patient record integration. Conversely, negative associations were noted with clinic head age and e-wallet usage. The findings stress targeted support for older clinic leaders in digital adaptation, highlight experienced leadership’s role, note distractions from financial technologies, emphasise social media’s digital readiness impact, and stress technological adoption’s importance, plus digital record-keeping benefits for clinics and patient care.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184716","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

Efficiency evaluation of filter sizes on graph convolutional neural networks for information extraction from receipts 从收据中提取信息的图卷积神经网络过滤器大小的效率评估

International Journal of Information Technology Pub Date : 2024-08-30 DOI: 10.1007/s41870-024-02089-1

An C. Tran, Bao Thai Le, Hai Thanh Nguyen

引用次数: 0

An IoMT data security framework with Hyperledger Fabric for smart cities 利用 Hyperledger Fabric 为智慧城市提供 IoMT 数据安全框架

International Journal of Information Technology Pub Date : 2024-08-29 DOI: 10.1007/s41870-024-02181-6

Saikat Samanta, Achyuth Sarkar, Sangeeta Kumari

引用次数: 0

A secured deep learning based smart home automation system 基于深度学习的安全智能家居自动化系统

International Journal of Information Technology Pub Date : 2024-08-29 DOI: 10.1007/s41870-024-02097-1

Chitukula Sanjay, Konda Jahnavi, Shyam Karanth

{"title":"A secured deep learning based smart home automation system","authors":"Chitukula Sanjay, Konda Jahnavi, Shyam Karanth","doi":"10.1007/s41870-024-02097-1","DOIUrl":"https://doi.org/10.1007/s41870-024-02097-1","url":null,"abstract":"With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184575","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

Automatic attack detection in IOT environment using relational auto encoder with enhanced ANFIS 利用关系自动编码器和增强型 ANFIS 自动检测物联网环境中的攻击行为

International Journal of Information Technology Pub Date : 2024-08-29 DOI: 10.1007/s41870-024-02141-0

R. M. Savithramma, C. L. Anitha, N. V. Sanjay Kumar, Subhash Kamble, B. P. Ashwini

{"title":"Automatic attack detection in IOT environment using relational auto encoder with enhanced ANFIS","authors":"R. M. Savithramma, C. L. Anitha, N. V. Sanjay Kumar, Subhash Kamble, B. P. Ashwini","doi":"10.1007/s41870-024-02141-0","DOIUrl":"https://doi.org/10.1007/s41870-024-02141-0","url":null,"abstract":"The Internet of Things (IoT) has recently become an important innovation in building smart environments. With any technology that relies on the Internet of Things model, security and privacy are seen as key issues. Many privacy and security concerns arise due to the various possibilities of intruders to attack the system. Due to the dynamic and heterogeneous nature of IoT devices and networks, we propose a novel approach for attack detection in IoT environments by combining two modifications based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). For the efficient extraction of features from input datasets, we use a Relational Auto Encoder (RAE) Network, followed by an enhanced version of the ANFIS model. ANFIS parameters have been optimized to use Gaussian kernel membership functions and the Enhanced Osprey optimization algorithm (EOOA) has been used to optimize initial ANFIS parameters. As part of the experimental analysis, two sets of datasets are used; these are NSL-KDD 99 and UNSW-NB15 datasets, which contain different kinds of attack labels such as DoS, probing, U2R, and R2L attacks. Performance metrics including accuracy, precision, recall, and F-measure are used to assess the effectiveness of our proposed scheme. As a result of this approach, we have demonstrated promising results in identifying attackers for IoT security applications, while also offering robustness and scalability.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184742","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

Abnormal behavior detection mechanism using deep learning for zero-trust security infrastructure 利用深度学习为零信任安全基础设施建立异常行为检测机制

International Journal of Information Technology Pub Date : 2024-08-28 DOI: 10.1007/s41870-024-02110-7

Hyun-Woo Kim, Eun-Ha Song

引用次数: 0

Low power and energy efficient design of ternary D-latch based on CNTFET-RRAM technology 基于 CNTFET-RRAM 技术的低功耗、高能效三元 D 型锁存器设计

International Journal of Information Technology Pub Date : 2024-08-28 DOI: 10.1007/s41870-024-02135-y

Tabassum Khurshid, Vikram Singh

引用次数: 0

Lung and colon classification using improved local Fisher discriminant analysis with ANFIS 利用改进的局部费舍尔判别分析和 ANFIS 对肺和结肠进行分类

International Journal of Information Technology Pub Date : 2024-08-28 DOI: 10.1007/s41870-024-02148-7

Amit seth, Vandana Dixit Kaushik

{"title":"Lung and colon classification using improved local Fisher discriminant analysis with ANFIS","authors":"Amit seth, Vandana Dixit Kaushik","doi":"10.1007/s41870-024-02148-7","DOIUrl":"https://doi.org/10.1007/s41870-024-02148-7","url":null,"abstract":"Cancer has a high mortality rate due to its aggressiveness, potential for metastasis, and heterogeneity. There are several types of cancers that are common throughout the world, including lung cancer and colon cancer. Radiologists have developed several expert systems to assist in the diagnosis of lung cancer over the years. However, this requires accurate research. Therefore, in this paper, an automatic lung and colon cancer classification model based on a machine learning algorithm is proposed. Initially, the histopathological images are collected from the dataset. Then, to reduce the noise present in the input image, we apply the adaptive median filter. After noise removal, we use novel feature extraction techniques, gray-level histogram (MGH) of moments, local binary pattern (LBP) features, and gray-level co-occurrence matrix (GLCM) features and morphological features to extract features. Since the large number of features is a major obstacle to the classification process, improved local Fisher discriminant analysis (ILFDA) is used to reduce the dimensionality after feature extraction. After feature selection, the selected features are given to an enhanced ANFIS classifier to classify an image as normal or abnormal. The performance of the proposed approach is analyzed based on different metrics. The proposed method is implemented in Python.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184587","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