International Journal of Information Technology最新文献_第5页

Automatic attack detection in IOT environment using relational auto encoder with enhanced ANFIS 利用关系自动编码器和增强型 ANFIS 自动检测物联网环境中的攻击行为

International Journal of Information Technology Pub Date : 2024-08-29 DOI: 10.1007/s41870-024-02141-0

R. M. Savithramma, C. L. Anitha, N. V. Sanjay Kumar, Subhash Kamble, B. P. Ashwini

{"title":"Automatic attack detection in IOT environment using relational auto encoder with enhanced ANFIS","authors":"R. M. Savithramma, C. L. Anitha, N. V. Sanjay Kumar, Subhash Kamble, B. P. Ashwini","doi":"10.1007/s41870-024-02141-0","DOIUrl":"https://doi.org/10.1007/s41870-024-02141-0","url":null,"abstract":"The Internet of Things (IoT) has recently become an important innovation in building smart environments. With any technology that relies on the Internet of Things model, security and privacy are seen as key issues. Many privacy and security concerns arise due to the various possibilities of intruders to attack the system. Due to the dynamic and heterogeneous nature of IoT devices and networks, we propose a novel approach for attack detection in IoT environments by combining two modifications based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). For the efficient extraction of features from input datasets, we use a Relational Auto Encoder (RAE) Network, followed by an enhanced version of the ANFIS model. ANFIS parameters have been optimized to use Gaussian kernel membership functions and the Enhanced Osprey optimization algorithm (EOOA) has been used to optimize initial ANFIS parameters. As part of the experimental analysis, two sets of datasets are used; these are NSL-KDD 99 and UNSW-NB15 datasets, which contain different kinds of attack labels such as DoS, probing, U2R, and R2L attacks. Performance metrics including accuracy, precision, recall, and F-measure are used to assess the effectiveness of our proposed scheme. As a result of this approach, we have demonstrated promising results in identifying attackers for IoT security applications, while also offering robustness and scalability.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184742","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

Abnormal behavior detection mechanism using deep learning for zero-trust security infrastructure 利用深度学习为零信任安全基础设施建立异常行为检测机制

International Journal of Information Technology Pub Date : 2024-08-28 DOI: 10.1007/s41870-024-02110-7

Hyun-Woo Kim, Eun-Ha Song

引用次数: 0

Low power and energy efficient design of ternary D-latch based on CNTFET-RRAM technology 基于 CNTFET-RRAM 技术的低功耗、高能效三元 D 型锁存器设计

International Journal of Information Technology Pub Date : 2024-08-28 DOI: 10.1007/s41870-024-02135-y

Tabassum Khurshid, Vikram Singh

引用次数: 0

Lung and colon classification using improved local Fisher discriminant analysis with ANFIS 利用改进的局部费舍尔判别分析和 ANFIS 对肺和结肠进行分类

International Journal of Information Technology Pub Date : 2024-08-28 DOI: 10.1007/s41870-024-02148-7

Amit seth, Vandana Dixit Kaushik

{"title":"Lung and colon classification using improved local Fisher discriminant analysis with ANFIS","authors":"Amit seth, Vandana Dixit Kaushik","doi":"10.1007/s41870-024-02148-7","DOIUrl":"https://doi.org/10.1007/s41870-024-02148-7","url":null,"abstract":"Cancer has a high mortality rate due to its aggressiveness, potential for metastasis, and heterogeneity. There are several types of cancers that are common throughout the world, including lung cancer and colon cancer. Radiologists have developed several expert systems to assist in the diagnosis of lung cancer over the years. However, this requires accurate research. Therefore, in this paper, an automatic lung and colon cancer classification model based on a machine learning algorithm is proposed. Initially, the histopathological images are collected from the dataset. Then, to reduce the noise present in the input image, we apply the adaptive median filter. After noise removal, we use novel feature extraction techniques, gray-level histogram (MGH) of moments, local binary pattern (LBP) features, and gray-level co-occurrence matrix (GLCM) features and morphological features to extract features. Since the large number of features is a major obstacle to the classification process, improved local Fisher discriminant analysis (ILFDA) is used to reduce the dimensionality after feature extraction. After feature selection, the selected features are given to an enhanced ANFIS classifier to classify an image as normal or abnormal. The performance of the proposed approach is analyzed based on different metrics. The proposed method is implemented in Python.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184587","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

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