International Journal of Information Technology最新文献

{"title":"Improved clustering techniques for paediatric cerebral palsy gait assessment during rehabilitation","authors":"Prateek Singhal, Rakesh Kumar Yadav","doi":"10.1007/s41870-024-02115-2","DOIUrl":"https://doi.org/10.1007/s41870-024-02115-2","url":null,"abstract":"<p>The gait abnormality may be the cause of various diseases like foot drop, lower back trembling, and osteoarthritis in the human body. The causes may affect body performance. The problem may be solved if we notice it between the ages of 2 and 20. Today's medical research struggles to identify normality and abnormality in children at a young age. The gait abnormalities depend on a complex neurological condition called cerebral palsy. The article proposes an improved fuzzy C-mean-PSO technique and also defines selection criteria for gait patterns, such as optimal number identification gait profiles, mean square error, silhouette coefficient, and Dunn index. The researcher used 156 patients’ data from the available O’Malley gait dataset for experimental purposes. We partitioned 156 patients into 5 different combinations. In the first two combinations, we applied conventional methods, and the next three employed proposed methods. Finally, we found the 91.6% CPI (Cluster Purity Index), that is greater than existing techniques. In the future, we can perform the proposed methods on various datasets. The findings indicate that employing clustering-based gait profiles improved fuzzy C-mean-PSO optimised using these methods can aid in measuring clinical rehabilitation for children with cerebral palsy.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184708","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":"Cyber-physical systems for hybrid braking control techniques in hybrid electric vehicles","authors":"Appalabathula Venkatesh, S. Phani Kumar, S. S. Kiran, K. Gurucharan","doi":"10.1007/s41870-024-02184-3","DOIUrl":"https://doi.org/10.1007/s41870-024-02184-3","url":null,"abstract":"<p>During the application of sudden braking in a transportation vehicle, safety is the most significant factor. When brakes are pressed suddenly, a vehicle’s wheels briefly lock, but the braking distance increases and steering control is ended up losing during that phase, but the antilock braking system (ABS) prevents the locking. As a result, a reliable and consistent control system is required to monitor the wheels and supply the adjustable pressure to the brakes. The integration of regenerative and mechanical braking is critical for the top-notch performance and trustworthy in safety features of the vehicle. In this context, cyber-physical systems (CPSs) can be used to develop hybrid control strategies for the braking system of HEVs. There are several braking control strategies available, with the slip control strategy outperforming the others in EV/HEV applications. The slip control strategy provides the variation between the measured motor speed with reference to the relative desired slip, which is internally controlled by the ABS control system. The current research work employs a variety of control strategies. To monitor the behavior of the implemented ABS, a test case is implemented with the HEV application.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184632","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":"Audio spectrogram analysis in IoT paradigm for the classification of psychological-emotional characteristics","authors":"Ankit Kumar, Sushil Kumar Singh, Indu Bhardwaj, Prakash Kumar Singh, Ashish Khanna, Biswajit Brahma","doi":"10.1007/s41870-024-02166-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02166-5","url":null,"abstract":"<p>Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events from external action units is one of the research subjects to explore various human behavior and their dependencies. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for some time and various interrogation sessions to finalize the emotional severity of an individual. The challenges in exploring human emotions propel the requirement of computer vision techniques in this field. The proposed study explicitly evaluates the recognition of psychological-emotional activities with the help of an audio spectrogram of people fetched through an IoT (Internet of Things) device comprising a microphone to investigate its correlation with psychological events. The audio samples are collected in an asymmetric environment where the chances of the noise are random. Noise cancellation, low power consumption, and sensitivity controls are some of the prominent features of the microphone IoT that have been used to extract raw audio samples. The proposed system follows the extraction of features such as mel-frequency cepstral coefficients (MFCC), harmonic to noise rate (HNR), zero crossing rate (ZCR), and Generative Adversarial Networks (GAN) from the audio spectrogram. The study uses a deep learning-based model containing a convolutional neural network model to recognize and classify different psychological-emotional stages including happiness, anger, disgust, surprise, fear, and sadness from audio spectrogram features. The average accuracy of the classification model for the recognition of all emotions is found to be 99.42% in a maximum of 312 iterations. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, improves the overall mental healthcare system.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184733","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":"Dynamic clustering based on Minkowski similarity for web services aggregation","authors":"Suad Kamil Ayfan, Dhiah Al-Shammary, Ahmed M. Mahdi, Fahim Sufi","doi":"10.1007/s41870-024-02174-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02174-5","url":null,"abstract":"<p>This research, introduces a new dynamic clustering method offering a new approach utilizing Minkowski Distance methods for calculating similarity of xml messages to effectively compress and aggregate them. The increase in Web services utilization has led to bottlenecks and congestion on network links with limited bandwidth. Furthermore, Simple Object Access Protocol (SOAP) is an eXtensible Markup Language (XML) based messaging system often utilized on the internet. It leads to interoperability by facilitating connection both users and their service providers across various platforms. The large amount and huge size of the SOAP messages being exchanged lead to congestion and bottlenecks. Aggregation tools for SOAP messages can effectively decrease the significant amount that traffic generated. This has shown a notable enhancement in performance. Enhancements can be made by using similarity methods. These techniques group together multiple SOAP messages that share a significant level of similarity. Present techniques utilizing grouping for aggregating XML messages have demonstrated efficiency and compression ratio limitations. Practically, the proposed model groups messages into clusters based on minimum distance, supporting Huffman (variable-length) and (fixed-length) encoding compressing for aggregating multiple compressed XML web messages into a single compact message. Generally, the suggested model’s performance has been evaluated through a comparison with K-Means, Principle Component Analysis (PCA) with K-Means, Hilbert, and fractal self-similarity clustering models. Minkowski distance clustering model has shown excellent performance, especially in all message sizes like small, medium, large, V.large. Technically, the model achieved superior average Compression Ratio and it has outperformed all other models.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184710","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":"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":"<p>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.</p>","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}

{"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":"<p>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 <i>T</i>-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.</p>","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}

{"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":"<p>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.</p>","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}

{"title":"Blockchain-enabled trust-based patient-centric electronic medical record model (TPC-EMR)","authors":"Sakshi Singh, Shampa Chakraverty","doi":"10.1007/s41870-024-02179-0","DOIUrl":"https://doi.org/10.1007/s41870-024-02179-0","url":null,"abstract":"<p>The transformative potential of telemedicine, secure data interchange, and blockchain technology combined with patient-centric healthcare systems is investigated in this study. We suggest a novel framework with telemedicine features for in-the-moment virtual consultations and a patient portal for scheduling appointments to track medical information and monitor health metrics. One of the main components of this framework is a trust-based feedback system, which creates trust ratings for medical professionals by gathering and evaluating patient feedback. This model seeks to improve healthcare outcomes and service delivery through fostering trust, promoting interoperability, and enhancing patient autonomy. The architecture, the way trust scores are calculated, how they affect patient care and potential future research directions are all assessed in this study.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184713","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":"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":"<p>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.</p>","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}

{"title":"Novel neurosymbolic artificial intelligence (NSAI) based algorithm to predict specific energy absorption in CoCrMo based architected materials","authors":"Akshansh Mishra, Vijaykumar S. Jatti, Dhruv A. Sawant, Ajay S. Visave","doi":"10.1007/s41870-024-02173-6","DOIUrl":"https://doi.org/10.1007/s41870-024-02173-6","url":null,"abstract":"<p>In this paper, two neurosymbolic-based methods—the neurosymbolic decision tree (DT) and the neurosymbolic XGBoost—are compared with a simple artificial neural network (ANN) to see how well they perform. This provides a novel comparison of prediction algorithms. The outcomes demonstrate that the neurosymbolic-based algorithms outperform other algorithms in terms of mean squared error (MSE) and R-squared (R²) value. Simple ANN gave values of 8.4 and 0.92, Neurosymbolic Decision tree gave values of 1.4 and 0.98, Neurosymbolic XGBoost gave values of 0.62 and 0.99 as MSE and R² respectively. When combined, symbolic and neurological components offer a new methodology that is more accurate and comprehensible. This work highlights the ways in which neurosymbolic techniques can be applied to improve predictive modeling in several domains, contributing to the growing body of research on the subject.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224073","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}