International Journal of Information Technology最新文献_第7页

Meta-styled CNNs: boosting robustness through adaptive learning and style transfer 元风格 CNN：通过自适应学习和风格转移提高鲁棒性

International Journal of Information Technology Pub Date : 2024-08-21 DOI: 10.1007/s41870-024-02150-z

Arun Prasad Jaganathan

{"title":"Meta-styled CNNs: boosting robustness through adaptive learning and style transfer","authors":"Arun Prasad Jaganathan","doi":"10.1007/s41870-024-02150-z","DOIUrl":"https://doi.org/10.1007/s41870-024-02150-z","url":null,"abstract":"Recent studies reveal that standard Convolutional Neural Networks (CNNs)—conventionally struggle—when the training data is corrupted, leading to significant performance drops with noisy inputs. Therefore, real-world data, influenced by various sources of noise like sensor inaccuracies, weather fluctuations, lighting variations, and obstructions, exacerbates this challenge substantially. To address this limitation—employing style transfer on the training data has been proposed by various studies. However, the precise impact of different style transfer parameter settings on the resulting model’s robustness remains unexplored. Therefore, in this study, we systematically investigated various magnitudes of style transfer applied to the training data, assessing their effectiveness in enhancing model robustness. Our findings indicate that the most substantial improvement in robustness occurs when applying style transfer with maximum magnitude to the training data. Furthermore, we examined the significance of the dataset’s composition from which the styles are derived. Our results demonstrate that utilizing a limited subset of just 64 diverse, randomly selected styles is adequate to observe desired performance generalization even under corrupted testing conditions. Therefore, instead of uniformly selecting styles from the dataset, we developed a probability distribution for selection. Notably, styles with higher selection probabilities exhibit qualitatively distinct characteristics compared to those with lower probabilities, suggesting a discernible impact on the model’s robustness. Utilizing style transfer with styles having maximum likelihood according to the learned distribution led to a 1.4% increase in mean performance under corruption compared to using an equivalent number of randomly chosen styles.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184582","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

Power-saving actionable recommendation system to minimize battery drainage in smartphones 省电可执行推荐系统，最大限度减少智能手机电池耗电量

International Journal of Information Technology Pub Date : 2024-08-20 DOI: 10.1007/s41870-024-02111-6

Yusuf Awad, Islam Hegazy, El-Sayed M. El-Horbaty

{"title":"Power-saving actionable recommendation system to minimize battery drainage in smartphones","authors":"Yusuf Awad, Islam Hegazy, El-Sayed M. El-Horbaty","doi":"10.1007/s41870-024-02111-6","DOIUrl":"https://doi.org/10.1007/s41870-024-02111-6","url":null,"abstract":"The issue of smartphone battery drainage is a common and widespread concern faced by numerous users. This problem arises due to the convergence of various factors, foremost among them being intensive active usage, the concurrent operation of numerous background applications, elevated screen brightness levels, persistent bad network connectivity, and the increased requirements on the device’s hardware elements. Mitigating this problem requires a strategic approach to reduce the processes running in the background, calibrate an optimal screen brightness, and disable idle or underutilized sensors and hardware components. Achieving an effective balance in managing these multifaceted aspects is vital for enhancing device efficiency, reducing battery drainage, and ultimately optimizing the overall usability of smartphones. In the context of this research, we present an innovative recommendation engine designed to empower users with actionable recommendations. These recommendations are actions to be taken in the system variable settings and interaction with the smartphone that will minimize battery drainage. Through rigorous testing in real-world scenarios, our recommendation engine has demonstrated tangible success, yielding an approximately daily smartphone usage extension of an average of 3.5 h in real-world testing, thus underscoring its practical efficacy and potential for substantial impact on user experience and device longevity.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184584","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

User-authenticated IoMT security model using blockchain authorization with data indexing and analysis 利用区块链授权和数据索引与分析的用户认证 IoMT 安全模型

International Journal of Information Technology Pub Date : 2024-08-20 DOI: 10.1007/s41870-024-02151-y

Y. Jani, P. Raajan

{"title":"User-authenticated IoMT security model using blockchain authorization with data indexing and analysis","authors":"Y. Jani, P. Raajan","doi":"10.1007/s41870-024-02151-y","DOIUrl":"https://doi.org/10.1007/s41870-024-02151-y","url":null,"abstract":"The Internet of Medical Things (IoMT) could significantly enhance conventional Healthcare (HC) services. To secure HC data, numerous data preservation and authentication techniques were developed. But, they could not effectively address security concerns and failed to retrieve data in minimal time. Thus, this work proposes a user Authenticated Security framework with blockchain-based authorization using an encoded access policy and smart contract with data indexing. Primarily, to book an appointment, the patient registers and login to the server. After consultation, the data is sensed and converted into cipher. This cipher is encrypted and uploaded to the hospital cloud server. In American Standard Code for Information Interchange(ASCII) binary Indexed Tree, the data’s location is indexed. In the meantime, a smart contract is created grounded on consultation details, which are converted to hashcode and stored in the blockchain. Afterward, by utilizing the patient’s and doctor’s data attributes, an encoded access policy is created. Now, the doctor login to the server, and a smart contract is created, which is converted to hash code. Grounded on the smart contract and encoded policy, blockchain-based authorization is performed. After verifying, the data is retrieved with the help of the indexed tree. Lastly, to provide a prescription, the attributes of the decrypted data are analyzed using Sigmoid Swish Long short-term memory (SS-LSTM). In experimental assessment, the proposed mechanism’s performance is proven with superior outcomes.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184585","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

Biomedical named entity recognition through improved balanced undersampling for addressing class imbalance and preserving contextual information 通过改进的平衡下采样技术识别生物医学命名实体，解决类不平衡问题并保留上下文信息

International Journal of Information Technology Pub Date : 2024-08-20 DOI: 10.1007/s41870-024-02137-w

S. M. Archana, Jay Prakash

{"title":"Biomedical named entity recognition through improved balanced undersampling for addressing class imbalance and preserving contextual information","authors":"S. M. Archana, Jay Prakash","doi":"10.1007/s41870-024-02137-w","DOIUrl":"https://doi.org/10.1007/s41870-024-02137-w","url":null,"abstract":"Biomedical Named Entity Recognition (Bio-NER) identifies and categorises the named entities of biomedical text data such as disease, chemical, protein, and gene. Since most of the biomedical data originates from the real world, the majority of data instances do not pertain to the specific named entity of interest. Therefore, this imbalance of data adversely impacts the performance of Bio-NER using machine learning models, as their learning objective is usually dominated by the majority of non-entity tokens. Various undersampling techniques have been introduced to address this issue. Balanced Undersampling (BUS) is one of the approaches which operates at the sentence level to enhance biomedical NER (Bio-NER). However, BUS lacks in preserving contextual information during the undersampling procedure. To overcome this limitation, we introduce an improved Balanced Undersampling method (iBUS) for Bio-NER. During the undersampling process, iBUS considers the importance of individual instances and generates a balanced dataset while retaining essential instances. To validate the effectiveness of the proposed method over competitive methods, we perform experiments using the NCBI disease dataset, CHEMDNER, and BC5CDR chemical datasets. The experimental results demonstrate the superiority of the proposed method in terms of the F1 score compared to competitive approaches.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184583","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

Sentiment-aware drug recommendations with a focus on symptom-condition mapping 以症状条件映射为重点的判决感知药物推荐

International Journal of Information Technology Pub Date : 2024-08-19 DOI: 10.1007/s41870-024-02091-7

E. Anbazhagan, E. Sophiya, R. Prasanna Kumar

{"title":"Sentiment-aware drug recommendations with a focus on symptom-condition mapping","authors":"E. Anbazhagan, E. Sophiya, R. Prasanna Kumar","doi":"10.1007/s41870-024-02091-7","DOIUrl":"https://doi.org/10.1007/s41870-024-02091-7","url":null,"abstract":"The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health Records (EHR) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis whereas researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel approach that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting certain drugs by evaluating sentiments and incorporating review ratings. With a Performance score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The performance of the model was evaluated based on accuracy, Receiver Operating Characteristic Curve (ROC)-Area Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for any type of symptom samples which is available in database.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184628","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

Leveraging CNN and principal component analysis for dynamic variance control in audio compression 利用 CNN 和主成分分析实现音频压缩中的动态差异控制

International Journal of Information Technology Pub Date : 2024-08-18 DOI: 10.1007/s41870-024-02155-8

Asish Debnath, Uttam Kr. Mondal

引用次数: 0

An intelligent and resolute Traffic Management System using GRCNet-StMO model for smart vehicular networks 利用 GRCNet-StMO 模型为智能车载网络打造智能果断的交通管理系统

International Journal of Information Technology Pub Date : 2024-08-16 DOI: 10.1007/s41870-024-02106-3

G. Sheeba, Jana Selvaganesan

{"title":"An intelligent and resolute Traffic Management System using GRCNet-StMO model for smart vehicular networks","authors":"G. Sheeba, Jana Selvaganesan","doi":"10.1007/s41870-024-02106-3","DOIUrl":"https://doi.org/10.1007/s41870-024-02106-3","url":null,"abstract":"One of the key components of a smart city is thought to be the traffic control system. Road traffic congestion is prevalent in big cities due to increasing population density and rising transportation in cities. A smart traffic control system using cutting-edge computational intelligence algorithms has been developed to address numerous challenges related to traffic management on road networks and to assist regulators in making sound decisions. The current endeavor seeks to develop a new type of Smart Traffic Management System (SmartTMS) using state-of-the-art deep learning and optimization methods. The hybrid Gated Recurrent Deep Convoluted Network (GRCNet) approach is applied to accurately forecast the traffic congestion from the smart vehicular networks. In order to improve the classifier's decision-making ability and prediction accuracy, the parameters of the deep learning algorithm are tuned using a revolutionary Starling Murmuration Optimizer (StMO) methodology. Moreover, traffic congestion in vehicle networks can be precisely diagnosed and decreased with a low error rate and high accuracy by using the GRCNet-StMO model combination. The proposed SmartTMS's main benefits are its ease of deployment, quick congestion forecast time, and minimal computing complexity. To evaluate the effectiveness of the suggested model, a comprehensive performance and comparison study is carried out in this work, taking into account a number of factors like error rate, accuracy, miss rate, and journey duration.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184631","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

Random grid visual cryptography scheme based on block encoding 基于块编码的随机网格视觉加密方案

International Journal of Information Technology Pub Date : 2024-08-16 DOI: 10.1007/s41870-024-02098-0

Maged Wafy

引用次数: 0

A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images 基于挤压激励胶囊网络 (SECNet) 模型的智能多模态框架，用于利用不同医学图像进行疾病诊断

International Journal of Information Technology Pub Date : 2024-08-16 DOI: 10.1007/s41870-024-02136-x

G. Maheswari, S. Gopalakrishnan

{"title":"A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images","authors":"G. Maheswari, S. Gopalakrishnan","doi":"10.1007/s41870-024-02136-x","DOIUrl":"https://doi.org/10.1007/s41870-024-02136-x","url":null,"abstract":"Computer-aided diagnosis has emerged as one of the main areas of study for radiology diagnosis and medical imaging in recent years. Also, developing a single prediction methodology for handling multiple types of medical images is remains one of the most significant issues in recent times. For handling various kinds of medical images, this research presents Smart Multimodal Disease Detection (SMD2), an innovative and powerful automated method. The proposed framework’s contribution is the ability to use various kinds of medical images to carry out an accurate and efficient disease diagnosis. The Woodpecker Mating Optimization Algorithm (WpMO) approach is used to optimally choose the most important features from the provided inputs, simplifying the classification process. In addition, the innovative Squeeze Excitation Capsule Network (SECNet) model is used to accurately identify and classify the disease class with a reduced computational time and complexity. A range of various medical imaging datasets, including X-ray, CT, and MRI, are considered for study in order to validate the performance outcomes of the proposed model. The results of the investigation indicate that the loss value of the proposed approach has dropped to 1.3, but its average accuracy has grown by 99%.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184670","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

Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection 催化脑电信号分析：挖掘机器学习智能 K 近邻离群点检测的潜力

International Journal of Information Technology Pub Date : 2024-08-16 DOI: 10.1007/s41870-024-02123-2

Abid Aymen, Salim El Khediri, Adel Thaljaoui, Moahmed Miladi, Abdennaceur Kachouri

引用次数: 0