International Journal of Information Technology最新文献_第4页

An implementation of GPU accelerated mapreduce: using hadoop with openCL for breast cancer detection and compute-intensive jobs GPU 加速 mapreduce 的实现：使用 hadoop 和 openCL 进行乳腺癌检测和计算密集型工作

International Journal of Information Technology Pub Date : 2024-09-02 DOI: 10.1007/s41870-024-02171-8

Hamza Ouhakki, Abdelali Elmoufidi

引用次数: 0

Alternative agriculture land-use transformation pathways by partial-equilibrium agricultural sector model: a mathematical approach 局部均衡农业部门模型：一种数学方法：替代性农业土地利用转型途径

International Journal of Information Technology Pub Date : 2024-08-31 DOI: 10.1007/s41870-024-02158-5

Malvika Kanojia, Prerna Kamani, Gautam Siddharth Kashyap, Shafaq Naz, Samar Wazir, Abhishek Chauhan

{"title":"Alternative agriculture land-use transformation pathways by partial-equilibrium agricultural sector model: a mathematical approach","authors":"Malvika Kanojia, Prerna Kamani, Gautam Siddharth Kashyap, Shafaq Naz, Samar Wazir, Abhishek Chauhan","doi":"10.1007/s41870-024-02158-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02158-5","url":null,"abstract":"Humanity’s progress in combating hunger, poverty, and child mortality is marred by escalating environmental degradation due to rising greenhouse gas emissions and climate change impacts. Despite positive developments, ecosystems are suffering globally. Regional strategies for mitigating and adapting to climate change must be viewed from a global perspective. The 2015 UN Sustainable Development Goals reflect the challenge of balancing social and environmental aspects for sustainable development. Agriculture, vital for food production, also threatens Earth systems. A study examines the interplay of land-use impacts, modeling crop and livestock trade, and their effects on climate, biodiversity, water, and land using a Partial-Equilibrium Agricultural Sector Model. Different scenarios involving taxing externalities related to Earth processes were tested. Results show synergies in reducing emissions, biodiversity loss, water use, and phosphorus pollution, driven by shifts in crop management. Nitrogen application and deforestation scenarios exhibit weaker synergies and more conflicts. The study offers insights into SDG interactions and the potential for sustainable farming.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184732","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

Indian fake currency detection using image processing and machine learning 利用图像处理和机器学习检测印度假币

International Journal of Information Technology Pub Date : 2024-08-31 DOI: 10.1007/s41870-024-02170-9

Sai Charan Deep Bandu, Murari Kakileti, Shyam Sunder Jannu Soloman, Nagaraju Baydeti

{"title":"Indian fake currency detection using image processing and machine learning","authors":"Sai Charan Deep Bandu, Murari Kakileti, Shyam Sunder Jannu Soloman, Nagaraju Baydeti","doi":"10.1007/s41870-024-02170-9","DOIUrl":"https://doi.org/10.1007/s41870-024-02170-9","url":null,"abstract":"The escalating production of counterfeit notes, facilitated by advancements in color printing and scanning, poses a significant global challenge impacting economies and security. This issue, prevalent in countries like India, has negative ramifications, including the funding of illegal activities and terrorism. Despite efforts, such as demonetization in 2016, counterfeits persist, necessitating innovative solutions. The proposed model introduces a fake note detection system utilizing computer vision and machine learning, specifically a Convolutional Neural Network (CNN). CNN effectively extracts intricate features from input data, showcasing its proficiency in pattern recognition. Notably, the system focuses on individual security features within banknotes, distinguishing it from other approaches that analyze entire note images. The primary goal is swift and accurate detection and reduction of counterfeit circulation, contributing to the overall security of the economy. The proposed model resulted in an impressive accuracy of 91.66% for all the six security features in the Indian denomination of Rs. 500, 95.25% for all the six security features in the Indian denomination of Rs. 200, 92.66% for all the six security features in the Indian denomination of Rs.100.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184731","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

A proactive grey wolf optimization for improving bioinformatic systems with high dimensional data 改进高维数据生物信息系统的前瞻性灰狼优化技术

International Journal of Information Technology Pub Date : 2024-08-31 DOI: 10.1007/s41870-024-02030-6

Ali Hakem Alsaeedi, Dhiah Al-Shammary, Suha Mohammed Hadi, Khandakar Ahmed, Ayman Ibaida, Nooruldeen AlKhazraji

{"title":"A proactive grey wolf optimization for improving bioinformatic systems with high dimensional data","authors":"Ali Hakem Alsaeedi, Dhiah Al-Shammary, Suha Mohammed Hadi, Khandakar Ahmed, Ayman Ibaida, Nooruldeen AlKhazraji","doi":"10.1007/s41870-024-02030-6","DOIUrl":"https://doi.org/10.1007/s41870-024-02030-6","url":null,"abstract":"This paper introduces a new methodology for optimization problems, combining the Grey Wolf Optimizer (GWO) with Simi-stochastic search processes. Intelligent optimizations represent an advanced approach in machine learning and computer applications, aiming to reduce the number of features used in the classification process. Optimizing bioinformatics datasets is crucial for information systems that classify data for intelligent tasks. The proposed A-Proactive Grey Wolf Optimization (A-GWO) solves stagnation in GWO by applying a dual search with a Simi-stochastic search. This target is achieved by distributing the population into two groups using a different search technique. The model's performance is evaluated using two benchmarks: the Evolutionary Computation Benchmark (CEC 2005) and seven popular biological datasets. A-GWO demonstrates highly improved efficiency in comparision to the original GWO and Particle Swarm Optimization (PSO). Specifically, it enhances exploration in 66% of CEC functions and achieves high accuracy in 70% of biological datasets.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224072","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

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