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

Polycystic ovary syndrome detection using optimized SVM and DenseNet 使用优化 SVM 和 DenseNet 检测多囊卵巢综合征

International Journal of Information Technology Pub Date : 2024-09-04 DOI: 10.1007/s41870-024-02143-y

E. Silambarasan, G. Nirmala, Ishani Mishra

{"title":"Polycystic ovary syndrome detection using optimized SVM and DenseNet","authors":"E. Silambarasan, G. Nirmala, Ishani Mishra","doi":"10.1007/s41870-024-02143-y","DOIUrl":"https://doi.org/10.1007/s41870-024-02143-y","url":null,"abstract":"Polycystic ovary syndrome (PCOS) is a complicated endocrine disease that significantly impacts the health of women, affecting fertility and leading to various critical conditions. Unfortunately, around 70% of PCOS cases remain undiagnosed, emphasizing the importance of early detection. Ultrasound imaging has emerged as a valuable tool for detecting polycystic ovaries, providing crucial details such as follicle count, size, and position. However, manual diagnosis through ultrasound imaging is laborious and prone to errors, highlighting the need for more objective diagnostic methods. In this study, we propose two distinct predictive models for PCOS detection, utilizing both text and image based datasets. Firstly, an Optimized Support Vector Machine based PCOS detection model is developed using text-based datasets. Secondly, we introduce an image dataset based PCOS detection model using DenseNet. Experimental results demonstrated the suggested models’ effectiveness in accuracy, recall, F-score, and precision for both developed methods. The results showed that the present approaches offer superior performance compared to other methods.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184711","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

Exploring the statistical and computational analysis of sleep stages across different age groups 探索不同年龄段睡眠阶段的统计和计算分析

International Journal of Information Technology Pub Date : 2024-09-04 DOI: 10.1007/s41870-024-02152-x

Vikas Dilliwar, Mridu Sahu

{"title":"Exploring the statistical and computational analysis of sleep stages across different age groups","authors":"Vikas Dilliwar, Mridu Sahu","doi":"10.1007/s41870-024-02152-x","DOIUrl":"https://doi.org/10.1007/s41870-024-02152-x","url":null,"abstract":"Sleep is a crucial part of a healthy life and good sleep may depend on various factors such as sleep duration, sleep efficiency, sleep architecture, sleep latency, sleep fragmentation, etc. Poor sleep quality may lead to the cause of many diseases and disorders. The present work is based on the study and analysis of the polysomnography (PSG) datasets, collected from 82 subjects including 45 females and 37 males. The present work measures sleep stages including Rapid Eye Movement (REM or R), wakefulness (W), Stage-1, Stage-2, and Stage-3/4 of the subjects with age groups of 20–39, 40–59, 60–79, and 80–100 years. This research investigates the average sleeping time percentage in each age group and focuses on the changes in sleep patterns. Furthermore, this investigation employs statistical measures including median, variance, and standard deviation to comprehensively understand the variability of sleep quality and sleep parameters within each age group. The T-tests and ANOVA tests within specific sleep stages for each age group have been measured to determine the significance of age-related variations in sleep parameters. The results appear valid regardless of age and may provide valuable information about the impact on sleep quality. Also, the algorithm has been implemented in a multi-core computing platform with a parallel processing approach and reduced the 96% computation time. The analysis of the present work provides essential information regarding sleep in different age groups, potentially useful for maintaining sleep quality with age.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"390 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184715","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

WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system WSETO：针对 NB-IoT 跟踪系统的野生证券交易所交易优化算法路由功能

International Journal of Information Technology Pub Date : 2024-09-03 DOI: 10.1007/s41870-024-02130-3

Sreeparnesh Sharma Sivadevuni, J. Naveen

{"title":"WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system","authors":"Sreeparnesh Sharma Sivadevuni, J. Naveen","doi":"10.1007/s41870-024-02130-3","DOIUrl":"https://doi.org/10.1007/s41870-024-02130-3","url":null,"abstract":"The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Power Wide Area Networks (LPWAN) have been efficient in the data acquisition and remote monitoring area however they failed to generate high data rates, low latency, and the consumption of low power. To solve these problems, NB-IoT technology has developed in long-term asset tracking and it replaces the Global Positioning System (GPS) with its ubiquitous coverage. In this research, the Wild Stock Exchange Trading Optimization technique (WSETO) is proposed for a routing-based NB-IoT tracking system. The WSETO is the combination of the Wild Geese Algorithm (WGA) and SETO. By employing WSETO, the routing to the relevant target location is established effectively. The existing techniques like Low Power Asset Tracking of NB-IoT (LoPATraN), Monitoring system based on NB-IoT and BeiDou System/GPS (BDS/GPS), and Narrowband Physical Uplink Shared Channel (NPUSCH) are used to compare the WSETO approach. In rounds with a value of 2000, the WSETO demonstrates a superior location error of 0.001 in comparison to existing methods such as LoPATraN, a monitoring system utilizing NB-IoT and BDS/GPS, as well as NPUSCH.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184714","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

Blockchain-enabled trust-based patient-centric electronic medical record model (TPC-EMR) 区块链支持的基于信任的以患者为中心的电子病历模型（TPC-EMR）

International Journal of Information Technology Pub Date : 2024-09-03 DOI: 10.1007/s41870-024-02179-0

Sakshi Singh, Shampa Chakraverty

引用次数: 0

Multilevel thresholding based image segmentation using Masi entropy and moth-flame optimization algorithm 使用马西熵和蛾焰优化算法进行基于多级阈值的图像分割

International Journal of Information Technology Pub Date : 2024-09-03 DOI: 10.1007/s41870-024-02167-4

Abdul Kayom Md Khairuzzaman

{"title":"Multilevel thresholding based image segmentation using Masi entropy and moth-flame optimization algorithm","authors":"Abdul Kayom Md Khairuzzaman","doi":"10.1007/s41870-024-02167-4","DOIUrl":"https://doi.org/10.1007/s41870-024-02167-4","url":null,"abstract":"This paper presents a multilevel thresholding based image segmentation technique using the generalized Masi entropy as the criteria to select the optimal thresholds. Moth-flame optimization (MFO) algorithm is utilized to efficiently search the multiple thresholds. Multilevel thresholding based image segmentation techniques require optimization of the search space. In this regard, the MFO algorithm is investigated in this work for its effectiveness in searching the multiple thresholds. The proposed technique is also compared with the particle swarm optimization algorithm based multilevel thresholding technique based on the Karur’s entropy function. The comparison is performed using standard benchmark image databases. Mean structural similarity (SSIM) index, feature similarity (FSIM) index, and peak signal to noise ratio (PSNR) are used to compare the quality of the segmented images. The experimental results suggest that the proposed MFO algorithm based multilevel thresholding technique performs better than the compared techniques. From the results it can be concluded that the proposed technique can be effectively used for multilevel thresholding based image segmentation applications.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184735","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

Novel neurosymbolic artificial intelligence (NSAI) based algorithm to predict specific energy absorption in CoCrMo based architected materials 基于神经符号人工智能（NSAI）的新型算法，用于预测钴铬钼基建筑材料的比能量吸收率

International Journal of Information Technology Pub Date : 2024-09-02 DOI: 10.1007/s41870-024-02173-6

Akshansh Mishra, Vijaykumar S. Jatti, Dhruv A. Sawant, Ajay S. Visave

引用次数: 0

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