International Journal of Information Technology最新文献_第9页

Efficient data-driven occupancy detection in office environments and feature impact analysis 办公环境中数据驱动的高效占用检测及特征影响分析

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02125-0

Harrou Fouzi, Kini K. Ramakrishna, Muddu Madakyaru, Sun Ying

{"title":"Efficient data-driven occupancy detection in office environments and feature impact analysis","authors":"Harrou Fouzi, Kini K. Ramakrishna, Muddu Madakyaru, Sun Ying","doi":"10.1007/s41870-024-02125-0","DOIUrl":"https://doi.org/10.1007/s41870-024-02125-0","url":null,"abstract":"Occupancy detection is crucial in optimizing building energy efficiency and enhancing occupant comfort. This study introduces an innovative data-driven approach for accurate occupancy detection in an office room environment. Specifically, the methodology combines the advantages of Independent Component Analysis (ICA) to extract essential features from multivariate data and Kantorovitch distance (KD)-based schemes for detection sensitivity. The KD scheme’s detection threshold is computed nonparametrically using kernel density estimation to enhance the sensitivity of occupancy detection. The efficacy of this strategy is evaluated utilizing publicly available data recorded during winter in Mons, Belgium, capturing vital environmental parameters such as temperature, humidity, light, and CO(_{2}) levels through specialized sensors. Results demonstrate that the ICA-KD approach achieves an averaged accuracy of 98.355%, surpassing conventional approaches like Principal Component Analysis (PCA)-based, ICA-based, and other state-of-the-art methods. Additionally, the study uses Shapley Additive exPlanations (SHAP) with XGBoost to explore the impact of input variables on occupancy detection, highlighting the influence of various factors under different testing conditions.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942775","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

Optimal air quality management using novel dual Mamdani and neuro fuzzy inference system for real-time accurate prediction 利用新型双马姆达尼和神经模糊推理系统进行实时精确预测，优化空气质量管理

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02116-1

Paritosh Kumar Yadav, Sudhakar Pandey

{"title":"Optimal air quality management using novel dual Mamdani and neuro fuzzy inference system for real-time accurate prediction","authors":"Paritosh Kumar Yadav, Sudhakar Pandey","doi":"10.1007/s41870-024-02116-1","DOIUrl":"https://doi.org/10.1007/s41870-024-02116-1","url":null,"abstract":"An accurate measure of the quality of the air in any given location is the air quality index(AQI). When calculating the AQI, important air pollutants such as SO2, NO2, ground-level O3, CO, and particle matter are taken into account. Numerous organizations worldwide compute these indices based on a range of parameters. In India, the Central Pollution Control Board(CPCBs) and State Pollution Control Board (SPCBs) monitor the air quality. Every pollutant is assigned a sub-index, and the aggregate of all these sub-indices is known as the AQI. Poor, fair, or acceptable air quality can be conveyed linguistically using the AQI, which is a numerical value. When the AQI rises, it is anticipated that a considerable segment of the population may have major health effects. The current research’s aims to calculate the levels of air pollutants in Raipur's four major parts of cities from December 21, 2023, to March 27, 2024. The traditional AQI is calculated using an equation. To determine the fuzzy air quality index, a fuzzy logic system is used, and membership functions are provided as input to the Noval Dual Mamdani fuzzy inference system (FIS). As a result, the research suggests a more dependable method for computing the fuzzy air quality index using fuzzy logic.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942780","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

NSS-ML: a Novel spectrum sensing framework using machine learning for cognitive radio IoT networks NSS-ML：利用机器学习的新型频谱感知框架，适用于认知无线电物联网网络

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02121-4

Nikhil Kumar Marriwala, Vinod Kumar Shukla, Manjula Shanbhog, Sunita Panda, Ruchi Kaushik, Deepak Rathore

{"title":"NSS-ML: a Novel spectrum sensing framework using machine learning for cognitive radio IoT networks","authors":"Nikhil Kumar Marriwala, Vinod Kumar Shukla, Manjula Shanbhog, Sunita Panda, Ruchi Kaushik, Deepak Rathore","doi":"10.1007/s41870-024-02121-4","DOIUrl":"https://doi.org/10.1007/s41870-024-02121-4","url":null,"abstract":"A key component of cognitive radio systems is spectrum sensing, which reduces coexistence problems and maximises spectrum efficiency. However, the introduction of multiple situations with distinct characteristics brought about by 5G communication presents problems for spectrum sensing to support a wide range of applications with high performance and flexible implementation. Inspired by these difficulties, a new method with a multi-layer extreme learning machine optimised for bats is presented in this study. This technique makes use of a variety of input vectors, such as channel ID, energy, distance, and received signal intensity, to enhance user categorization and sensing capabilities. Moreover, we compare the proposed method with the state-of-the-art spectrum sensing approaches in order to evaluate its effectiveness in 5G situations, especially in healthcare applications. Evaluation metrics including channel detection probability, sensitivity, and selectivity are carefully examined. The findings unequivocally prove the suggested spectrum sensing approach’s superiority over current methods and highlight its potential for smooth incorporation into a variety of 5G applications.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942772","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 Medical Image Security: A Deep Learning Approach with Cloud-based Color Space Scrambling 增强医学图像安全性：基于云的色彩空间扰乱深度学习方法

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02109-0

Aswathy K. Cherian, Serin V. Simpson, M. Vaidhehi, Ramaprabha Marimuthu, M. Shankar

{"title":"Enhancing Medical Image Security: A Deep Learning Approach with Cloud-based Color Space Scrambling","authors":"Aswathy K. Cherian, Serin V. Simpson, M. Vaidhehi, Ramaprabha Marimuthu, M. Shankar","doi":"10.1007/s41870-024-02109-0","DOIUrl":"https://doi.org/10.1007/s41870-024-02109-0","url":null,"abstract":"Progress in wisdom medicine has been driven by advancements in big data, cloud computing, and artificial intelligence, enabling the accumulation of valuable information and insights. However, the increasing reliance on cloud-based storage and transmission of medical images has raised significant concerns regarding information security. The risk of unauthorized access to patients' private data poses a considerable obstacle to medical research advancement. Thus, safeguarding patient data in cloud environments is imperative. Color space-based scrambling algorithms (CSSA) are gaining traction for multimedia data encryption due to their compatibility with JPEG and reduced processing requirements. However, traditional CSSA methods rely on colorful images to optimize security, limiting their applicability in fields like medical image processing where color images may be scarce. This study seeks to integrate CSSA image encryption with Multilayer Perceptron (MLP)-based techniques for securing medical images. Additionally, a noise-based data augmentation method is developed to address data scarcity in medical image analysis. Security analysis and temporal complexity assessments are employed to evaluate the effectiveness of the proposed MLP-CSSA deep learning model in encrypting medical images. Results demonstrate robust security in encrypting both grayscale and color medical images, with the proposed MLP-CSSA method outperforming existing encryption techniques.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"391 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184634","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

Intelligent proportional-integral-derivative control techniques for accelerator leg of robot driver 机器人驾驶员加速腿的智能比例积分派生控制技术

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02131-2

Harsh Goud, Vibha Goud, Akshat Singh Chauhan

引用次数: 0

Prioritizing flows for internet of things built on visible light communication 优先考虑基于可见光通信的物联网流量

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02146-9

B. R. Vatsala, C. Vidyaraj, M. R. Rashmi

{"title":"Prioritizing flows for internet of things built on visible light communication","authors":"B. R. Vatsala, C. Vidyaraj, M. R. Rashmi","doi":"10.1007/s41870-024-02146-9","DOIUrl":"https://doi.org/10.1007/s41870-024-02146-9","url":null,"abstract":"Internet of things (IoT) consists of nodes with constraints concerning size, battery life, storage, processing, etc. Many IoT applications such as health monitoring systems generate huge amount of data that must be transmitted to destinations without delay during critical situations. Since IoT nodes have very small storage capacity, to transfer big data there is a requirement for a high bandwidth wireless technology such as visible light communication (VLC) which is harmless. Also flows that carry critical data must not be affected during congestion and must be given priority. The existing Transmission Control Protocol (TCP) has good congestion control algorithms but none of them consider the priority of flows during flow control. A Priority Queue based Flow Control Protocol named FCP_PQ is developed by providing priority to flows that carry critical data in high bandwidth network. The protocol developed ensures that only the flows that carry critical data are given priority over other flows during congestion by exhibiting an increase of 210 Kbps in case of goodput, 1.94% towards packet delivery ratio (PDR) and 4 Mega Bits transmission over 100 s time period in error free context and similar outcome is achieved in error-prone context compared to other flows.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942773","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

Spatio-temporal Weber Gradient Directional feature for visual and audio-visual phrase recognition systems 用于视觉和视听短语识别系统的时空韦伯梯度方向特征

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02138-9

Salam Nandakishor, Debadatta Pati

{"title":"Spatio-temporal Weber Gradient Directional feature for visual and audio-visual phrase recognition systems","authors":"Salam Nandakishor, Debadatta Pati","doi":"10.1007/s41870-024-02138-9","DOIUrl":"https://doi.org/10.1007/s41870-024-02138-9","url":null,"abstract":"Visual phrase recognition needs lip movement related visual features, while audio-visual phrase recognition requires both acoustic and visual features. In this work, we propose a novel visual feature; Spatio-temporal Weber Gradient Directional (SWGD) to effectively represent the micro-patterns of lip movements. The proposed visual feature is obtained by using micro-texture information; local differential excitation, gradient orientation, and gradient directional information. Experiments are conducted using standard OuluVS database. Polynomial kernel based support vector machine (SVM) classifier is employed, as it provides relatively better performance. The SWGD extracted from (2times 5times 3) video block size provides higher performance of 73.9%. Additionally, we explore twelve distinct local descriptors commonly employed in face recognition and utilize them for the first time in a comparative study of phrase recognition. SWGD performs better than these twelve distinct features but has higher dimension of 4320. By reducing the dimension to 100 using the soft locality preserving map (SLPM), performance improved from 73.9 to 81.3%. The dimensionally reduced SWGD (SWGD(_{text {SLPM}})) outperforms other state-of-the-art visual features mentioned in this paper. This shows the benefit of the salient micro-texture information considered in the proposed feature but neglected in state-of-the-art features. We observe that the SWGD(_{text {SLPM}}) feature has high discriminative ability to represent distinct lip movement patterns for different phrases. Mel-frequency cepstral coefficient (MFCC) based audio phrase recognizer performance degrades as the signal-to-noise level decreases. Including the SWGD(_{text {SLPM}}) visual feature and Glottal MFCC (GMFCC) excitation source feature improves performance by 3.6%, reflecting noise robustness.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942767","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

Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks 使用双变量深度神经网络在 mapreduce 中进行糖尿病慢性病预测的综合正常判别分析

International Journal of Information Technology Pub Date : 2024-08-11 DOI: 10.1007/s41870-024-02139-8

R. Ramani, D. Dhinakaran, S. Edwin Raja, M. Thiyagarajan, D. Selvaraj

{"title":"Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks","authors":"R. Ramani, D. Dhinakaran, S. Edwin Raja, M. Thiyagarajan, D. Selvaraj","doi":"10.1007/s41870-024-02139-8","DOIUrl":"https://doi.org/10.1007/s41870-024-02139-8","url":null,"abstract":"This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-RDNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal Discriminative Preprocessing Model (NDPM) and bivariant regressive deep artificial neural network with MapReduce (BRDANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-RDNMR conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRDANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942776","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 privacy-preserving approach for detecting smishing attacks using federated deep learning 利用联合深度学习检测网络钓鱼攻击的隐私保护方法

International Journal of Information Technology Pub Date : 2024-08-11 DOI: 10.1007/s41870-024-02144-x

Mohamed Abdelkarim Remmide, Fatima Boumahdi, Bousmaha Ilhem, Narhimene Boustia

引用次数: 0

A novel approach for the enhancement of payload capacity in pixel value differencing image steganography schemes 提高像素值差分图像隐写术方案有效载荷容量的新方法

International Journal of Information Technology Pub Date : 2024-08-11 DOI: 10.1007/s41870-024-02114-3

C. D. Nisha, Thomas Monoth

引用次数: 0