International Journal of Information Technology最新文献_第6页

AI-powered dining: text information extraction and machine learning for personalized menu recommendations and food allergy management 人工智能助力餐饮：文本信息提取和机器学习用于个性化菜单推荐和食物过敏管理

International Journal of Information Technology Pub Date : 2024-08-28 DOI: 10.1007/s41870-024-02154-9

Samiha Brahimi

引用次数: 0

A semantic approach for sarcasm identification for preventing fake news spreading on social networks 识别讽刺的语义方法，防止假新闻在社交网络上传播

International Journal of Information Technology Pub Date : 2024-08-27 DOI: 10.1007/s41870-024-02156-7

Fethi Fkih, Delel Rhouma, Hajar Alghofaily

{"title":"A semantic approach for sarcasm identification for preventing fake news spreading on social networks","authors":"Fethi Fkih, Delel Rhouma, Hajar Alghofaily","doi":"10.1007/s41870-024-02156-7","DOIUrl":"https://doi.org/10.1007/s41870-024-02156-7","url":null,"abstract":"Misinterpreting satirical posts can contribute to the spread of misinformation and potentially be a source of what is commonly referred to as “fake news”. Satire is a form of humor that often involves exaggeration, irony, or ridicule to comment on or criticize a particular subject. While satirical content is not intended to be taken literally, there are instances where individuals may misinterpret it, leading to the dissemination of false information. In fact, we can reduce the spread of fake news by preventing people from misinterpreting satirical posts. However, sarcasm recognition is considered a challenging task in the Sentiment Analysis domain. Even for humans, it can be difficult to recognize irony and sarcasm, which conveys a sharp, bitter remark or criticism in ambiguous and unclear natural language. This makes the identification much more difficult for an automated model. In this paper, we have carried out an in-depth literature review about the main approaches used for sarcasm detection and especially those based on Machine Learning (ML) models. Then, a study was conducted with a series of binary classification models that exploit a variety of statistical and semantic features. Our experiments have been carried out on twitter dataset obtained from SemEval-2018 Task 3. An extensive evaluation of each set of classifiers demonstrates the efficiency of our proposed model in detecting and identifying sarcastic content in tweets. Finally, we compared the performance of machine learning models using our proposed features with our baseline and state-of-the-art on the same dataset. By using Support Vector Machine (SVM) model and the proposed features, we outperform the state-of-the-art and we obtained an accuracy of 79.46% with a F-score equal to 79.66% which considered a promising result in this field.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224093","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 mental health prognosis: an investigation of advanced hybrid classifiers with cutting-edge feature engineering and fusion strategies 加强心理健康预后：采用尖端特征工程和融合策略的高级混合分类器研究

International Journal of Information Technology Pub Date : 2024-08-27 DOI: 10.1007/s41870-024-02092-6

Mohammad Ubaidullah Bokhari, Gaurav Yadav, Zeyauddin, Shahnwaz Afzal

{"title":"Enhancing mental health prognosis: an investigation of advanced hybrid classifiers with cutting-edge feature engineering and fusion strategies","authors":"Mohammad Ubaidullah Bokhari, Gaurav Yadav, Zeyauddin, Shahnwaz Afzal","doi":"10.1007/s41870-024-02092-6","DOIUrl":"https://doi.org/10.1007/s41870-024-02092-6","url":null,"abstract":"Mental health disorders present a significant global challenge, requiring early detection for effective intervention. This research explores the comparative performance of two advanced hybrid classifiers against conventional machine learning models. Introducing an innovative hybrid classifier framework, we combine Support Vector Machines with Neural Networks (Hybrid_1) and Random Forests with Gradient Boosting Machines (Hybrid_2), creating synergistic combinations of traditional and ensemble learning approaches. Using this novel fusion technique, we conduct a comprehensive analysis, emphasizing customized feature engineering strategies tailored for mental health assessment. Evaluation on the Mental_health dataset demonstrates the superior performance of hybrid classifiers, achieving accuracy rates of 86.69% and 93.54% for Hybrid_1 and Hybrid_2, respectively. These results highlight the potential of hybrid classifiers in mental health prediction and emphasize the crucial role of feature engineering in model optimization. Our pioneering hybrids, Hybrid_1 and Hybrid_2, represent a breakthrough, seamlessly integrating Support Vector Machines with Neural Networks and Random Forests with Gradient Boosting Machines, respectively. Distinguished from conventional approaches, our hybrids leverage the combined strengths of diverse algorithms, addressing challenges associated with complex feature relationships and dataset adaptability. This study not only showcases the promise of hybrid classifiers in mental health assessment but also provides valuable insights into feature selection and model interpretability, enhancing our understanding of this critical domain.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224092","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

Improving LSTM forecasting through ensemble learning: a comparative analysis of various models 通过集合学习改进 LSTM 预测：各种模型的比较分析

International Journal of Information Technology Pub Date : 2024-08-27 DOI: 10.1007/s41870-024-02157-6

Zishan Ahmad, Vengadeswaran Shanmugasundaram, Biju, Rashid Khan

{"title":"Improving LSTM forecasting through ensemble learning: a comparative analysis of various models","authors":"Zishan Ahmad, Vengadeswaran Shanmugasundaram, Biju, Rashid Khan","doi":"10.1007/s41870-024-02157-6","DOIUrl":"https://doi.org/10.1007/s41870-024-02157-6","url":null,"abstract":"Supply chain management involves managing the entire manufacturing process, from purchasing supplies to delivering the final product. Demand forecasting helps businesses predict future customer demand by analyzing historical data and market patterns. While various papers discuss optimizing models, this research compares several machine learning models, such as ARIMA, SARIMA, and deep learning models like RNN, LSTM, GRU, and BLSTM. It also extends to approaches like ensemble learning with the LSTM model, discussing how ensemble learning can further improve the LSTM model. This paper explores ensemble learning in two ways: a) without model pruning, averaging all generated models, and b) with model pruning, removing underperforming models and averaging top performers. Experiments conducted on a public dataset from the University of Chicago achieved a very low RMSE loss of 9.26 on the LSTM model improved via ensemble learning with model pruning. This ensemble approach with model pruning improved accuracy in predicting future customer demand, and a complete pipeline integrating visualization and a notification system was developed.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184577","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

Comparative investigation of group velocity dispersion with nonlinear phase modulation in fiber optic WDM transmission 光纤波分复用传输中非线性相位调制的群速度色散比较研究

International Journal of Information Technology Pub Date : 2024-08-25 DOI: 10.1007/s41870-024-02145-w

Nasrin Sultana, M. S. Islam

{"title":"Comparative investigation of group velocity dispersion with nonlinear phase modulation in fiber optic WDM transmission","authors":"Nasrin Sultana, M. S. Islam","doi":"10.1007/s41870-024-02145-w","DOIUrl":"https://doi.org/10.1007/s41870-024-02145-w","url":null,"abstract":"WDM system transmission efficiency is deteriorated by the combined influence of cross phase modulation (XPM) and group velocity dispersion (GVD) of first and second order. This degradation occurs as the channel bulk, light intensity, speed of transmission, and wavelength count frequencies increase. Analysis of the pulse broadening factor, standardized outturn, and resolving the nonlinear Schrödinger equation (NLSE) is conducted in this study. The influence of XPM on higher order GVD is reflected. The impact of broadcast limit and different absorbed powers (10 mW to 120 mW) at various transmission speeds (10 Gbps and 40 Gbps) are assessed utilizing large effective area fiber (LEAF) and standard single mode fiber (SSMF). The first- and second order GVD XPM impacts are the only ones that influence emitted oscillation. GVD's second-order consequences are not perceptible at close grips (⁓10 km) and low throughput (10 Gbps) but become perceptible and affect system performance at bit rates of 40 Gbps and above. The study found that transmission rate and fiber span have a stronger impression on duration than input dominance. The SSMF and LEAF consequences are obtained by rigorous derivation and numerical simulation at the consistent throughput and absorbed power managing the split-phase Fourier method. XPM has a stronger optimistic impact on GVD in SSMF fibers than LEAF fibers by 2 km. Due to their ability to quantify the degree of performance degradation emanating from XPM effects with first- and second order GVD, the findings of this work may prove useful in the design of high-speed, long-distance WDM fiber-optic transmission links.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224096","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

Unveiling the epilepsy enigma: an agile and optimal machine learning approach for detecting inter-ictal state from electroencephalogram signals 揭开癫痫之谜：从脑电图信号中检测发作间期状态的敏捷优化机器学习方法

International Journal of Information Technology Pub Date : 2024-08-23 DOI: 10.1007/s41870-024-02078-4

Shoibolina Kaushik, Mamatha Balachandra, Diana Olivia, Zaid Khan

{"title":"Unveiling the epilepsy enigma: an agile and optimal machine learning approach for detecting inter-ictal state from electroencephalogram signals","authors":"Shoibolina Kaushik, Mamatha Balachandra, Diana Olivia, Zaid Khan","doi":"10.1007/s41870-024-02078-4","DOIUrl":"https://doi.org/10.1007/s41870-024-02078-4","url":null,"abstract":"Epilepsy is a chronic neurological disorder characterized by the occurrence of paroxysmal recurrent seizures, which are caused by abnormal electrical activity in the brain. Seizures vary widely in their presentation, depending on the specific region of the brain involved and the extent of the abnormal electrical discharges. The disease can affect cognitive function posing a serious threat to the patients’ lives. Epilepsy causes emotional and behavioral changes, along with sleep disorders and migraines, leading to social isolation and discrimination. Timely administration of medication can cure most cases of epilepsy. However, identifying epileptic patients requires reviewing multiple EEG signal sheets, which can delay disease prediction. Therefore, the aim of our study is to apply simplistic machine learning algorithms that can study the EEG signal data swiftly to identify individuals in seizure, inter-ictal, and normal states, thereby aiding in medical diagnosis. The novelty of this study lies in the utilization of pre-built methods and develop a fast and efficient model that is lightweight and easy to integrate in healthcare to provide relief to epileptic patients. While previous studies have achieved high accuracy, the discussion involving time complexity of their models has been scarce. Given the importance of timely medication in managing epilepsy, it is crucial to consider the runtime of the model rather than solely focusing on accuracy. Therefore, a model that balances both a short runtime (2.9 min) and a satisfactory accuracy (97.46%) has been developed in this project. Integration of this project's findings will catalyze transformative changes within the healthcare industry, enabling healthcare professionals to detect epilepsy at earlier stages and provide timely interventions, ultimately fostering a system that prioritizes precision, innovation, and improved patient outcomes.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184578","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

VDRNet19: a dense residual deep learning model using stochastic gradient descent with momentum optimizer based on VGG-structure for classifying dementia VDRNet19：使用基于 VGG 结构的随机梯度下降与动量优化器的密集残差深度学习模型，用于痴呆症分类

International Journal of Information Technology Pub Date : 2024-08-23 DOI: 10.1007/s41870-024-02103-6

M. Pandiyarajan, R. S. Valarmathi

{"title":"VDRNet19: a dense residual deep learning model using stochastic gradient descent with momentum optimizer based on VGG-structure for classifying dementia","authors":"M. Pandiyarajan, R. S. Valarmathi","doi":"10.1007/s41870-024-02103-6","DOIUrl":"https://doi.org/10.1007/s41870-024-02103-6","url":null,"abstract":"Dementia disease is a syndrome caused by various disorders and conditions that affect the brain which causes gradual decline in neurological function commonly observed in older individuals. The disease is categorized into three stages in our research: Mild dementia (MD), Non-dementia (ND) and very mild dementia (VMD). Magnetic Resonance Imaging (MRI) scan of the brain is used for diagnosing dementia. In this research, a dense residual deep learning model using stochastic gradient descent with momentum optimizer based on VGG-structure for classifying dementia (VDRNet19) is proposed, which can detect all three stages of dementia The proposed model is trained and tested with the Open Access Series of Imaging and Studies (OASIS) dataset. In this work, the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement method is employed to preprocess the raw for analysis. In order to confront the imbalance in dataset, augmentation techniques are used. As a result, a balanced dataset comprising a total of 1941 images across the three classes are obtained. Initially, six existing models including DenseNet201, VGG19, ResNet152, AlzheimerNet [13], MobileNetV2 and ensemble of pretrained networks were trained and tested to attain 93.84%, 92.42%, 91.1%, 89.73%, 87.67% and 94.86% of test accuracies respectively. DenseNet201, VGG19, ResNet152 yields the highest accuracy, which is the backbone to design the proposed model. VDRNet19 using optimizer as stochastic gradient descent with momentum, 0.01 as learning rate, achieves the highest testing accuracy of 97.26%. This study compared six pre-trained models alongside the proposed model in terms of performance metrics to determine if the VDRNet19 model excels in classifying the three classes. An ablation study was conducted to validate the chosen hyperparameters. Results indicate that the proposed model surpasses traditional methods in classifying dementia stages from brain MRI scan images.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184580","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

Weight factor and priority-based virtual machine load balancing model for cloud computing 基于权重因子和优先级的云计算虚拟机负载均衡模型

International Journal of Information Technology Pub Date : 2024-08-23 DOI: 10.1007/s41870-024-02119-y

E. Suganthi, F. Kurus Malai Selvi

{"title":"Weight factor and priority-based virtual machine load balancing model for cloud computing","authors":"E. Suganthi, F. Kurus Malai Selvi","doi":"10.1007/s41870-024-02119-y","DOIUrl":"https://doi.org/10.1007/s41870-024-02119-y","url":null,"abstract":"Cloud computing enables individuals and businesses to buy services as needed. Numerous services are available through the paradigm, including online services that are easily accessible, platforms for deploying applications, and storage. One major problem in the cloud is load balancing (LB), making it difficult to guarantee application performance to the Quality of Service (QoS) measurement and adhere to the Service Level Agreement (SLA) document as cloud providers require of businesses. Equitable workload distribution among servers is a challenge for cloud providers. By effectively using virtual machines' (VMs) resources, an effective load-balancing approach should maximize and guarantee high user satisfaction. This research paper proposes an efficient load-balancing model for cloud computing using a weight factor and priority-based approach. This approach efficiently allocates the VM to the Physical Machine (PM). The main objective of this approach is to maintain QoS while reducing power usage, resource waste, and migration overhead. Based on the resources (CPU, RAM, Bandwidth), the PM current condition is computed using the suggested PM load identification algorithm based on the resource weight factor. The priority-based VM allocation model determines the ideal solution for selecting the suitable PM for the VM. The recommended method is simulated using the Cloudsim toolbox, and performance in terms of EC and SLA breaches is assessed using the PlanetLab workload. Ultimately, the experimental findings demonstrate that the suggested algorithm significantly improves SLAV and energy usage compared to existing approaches.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184579","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

Optimizing accuracy and efficiency in real-time people counting with cascaded object detection 利用级联对象检测优化实时人员计数的准确性和效率

International Journal of Information Technology Pub Date : 2024-08-23 DOI: 10.1007/s41870-024-02153-w

M. Raviraja Holla, D. Suma, M. Darshan Holla

{"title":"Optimizing accuracy and efficiency in real-time people counting with cascaded object detection","authors":"M. Raviraja Holla, D. Suma, M. Darshan Holla","doi":"10.1007/s41870-024-02153-w","DOIUrl":"https://doi.org/10.1007/s41870-024-02153-w","url":null,"abstract":"Growing concerns about public safety have driven the demand for real-time surveillance, particularly in monitoring systems like people counters. Traditional methods heavily reliant on facial detection face challenges due to the complex nature of facial features. This paper presents an innovative people counting system known for its robustness, utilizing holistic bodily characteristics for improved detection and tallying. This system achieves exceptional performance through advanced computer vision techniques, with a flawless accuracy and precision rate of 100% under ideal conditions. Even in challenging visual conditions, it maintains an impressive overall accuracy of 98.42% and a precision of 97.51%. Comprehensive analyses, including violin plot and heatmaps, support this outstanding performance. Additionally, by assessing accuracy and execution time concerning the number of cascading stages, we highlight the significant advantages of our approach. Experimentation with the TUD-Pedestrian dataset demonstrates an accuracy of 94.2%. Evaluation using the UCFCC dataset further proves the effectiveness of our approach in handling diverse scenarios, showcasing its robustness in real-world crowd counting applications. Compared to benchmark approaches, our proposed system demonstrates real-time precision and efficiency.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184586","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

Anomaly detection in cyber-physical systems using actuator state transition model 利用执行器状态转换模型进行网络物理系统异常检测

International Journal of Information Technology Pub Date : 2024-08-23 DOI: 10.1007/s41870-024-02128-x

Rajneesh Kumar Pandey, Tanmoy Kanti Das

{"title":"Anomaly detection in cyber-physical systems using actuator state transition model","authors":"Rajneesh Kumar Pandey, Tanmoy Kanti Das","doi":"10.1007/s41870-024-02128-x","DOIUrl":"https://doi.org/10.1007/s41870-024-02128-x","url":null,"abstract":"Cyber-physical systems (CPS) are vulnerable to cyber attacks which disrupt the operations of the associated physical process. Sensors are deployed in CPS to observe its functioning and control systems like actuators, Remote Terminal Units (RTU), programmable logic controllers (PLC), etc., are used to change the state of the CPS. Any abnormal state transitions due to cyber attack or natural fault may not be detected by the traditional Intrusion Detection System (IDS). Behavior specification-based IDS, which employs laws of physics to detect the intrusion, may be helpful in this context. However, specifying acceptable behaviors based on the laws of physics for all the installed control systems for a complex CPS like a smart grid, water treatment plant, etc., is a challenging task. Here, we employ a data-driven strategy to model the behavior of each control system installed in a CPS. Later, we use the models to predict the acceptable states of all the control systems. We utilize an AI-based classifier to model control systems such as actuators. Subsequently, we juxtapose the actual states of the actuators with their predicted states, examining how this combination correlates with the overall state of the CPS to identify anomalies. Typically, there should be a strong correlation between predicted and actual states, making the Hamming distance between them a crucial factor in our experimentation. To establish the relationship between controller states and CPS states, we employ a novel deep neural network-based approach for classification. Experimental validation of our approach leverages data from a water treatment testbed, where we achieve superior performance compared to the most state-of-the-art methods, achieving a F1-score of 0.96.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184581","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