International Journal of Intelligent Networks最新文献_第5页

Efficient breast cancer detection via cascade deep learning network 基于级联深度学习网络的癌症高效检测

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.02.001

Bita Asadi, Qurban Memon

{"title":"Efficient breast cancer detection via cascade deep learning network","authors":"Bita Asadi, Qurban Memon","doi":"10.1016/j.ijin.2023.02.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.02.001","url":null,"abstract":"<div><p>Breast calcifications or irregular tissue growth are major health concerns that can lead to breast cancer. To enable early management, which significantly lowers death rates, it is crucial to perform screening and determine if a tumor is benign or malignant. Building a cascade network model that bases predictions on the shape, pattern, and spread of the tumor is how this research approaches the challenge. Pre-processing of images, followed by segmentation and classification, are common methods to accomplish this. The strategy in this research employs a cascade network with UNet architecture for segmentation with a ResNet backbone for classification. To enable classification to make predictions, segmentation process involves separating tumor from the image in the form of a mask. The segmentation model's F1-score measurement came out to be 97.30%. The final decision-making layer's neural network is a straightforward 8-layer network, which follows the ResNet50 model. The proposed model's classification accuracy was 98.61%, with F1 score of 98.41%. Comparative evaluations are conducted together with the comprehensive experimental results.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 46-52"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194630","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: 3

Classification of network slicing threats based on slicing enablers: A survey 基于切片使能因子的网络切片威胁分类：一项调查

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.04.002

Mohammad J.K. Abood , Ghassan H. Abdul-Majeed

引用次数: 1

A novel deep neural network heartbeats classifier for heart health monitoring 一种用于心脏健康监测的新型深度神经网络心跳分类器

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2022.11.001

Velagapudi Swapna Sindhu, Kavuri Jaya Lakshmi, Ameya Sanjanita Tangellamudi, K. Ghousiya Begum

{"title":"A novel deep neural network heartbeats classifier for heart health monitoring","authors":"Velagapudi Swapna Sindhu, Kavuri Jaya Lakshmi, Ameya Sanjanita Tangellamudi, K. Ghousiya Begum","doi":"10.1016/j.ijin.2022.11.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2022.11.001","url":null,"abstract":"<div><p>The electrocardiogram (ECG) is a very useful diagnostic tool to examine the functioning of the heart and to detect myocardial infarction (MI) and arrhythmias. It contains the records of the electrical signal of the heart and it is an investigation tool to check the heart's rhythm and thereby analyze heartbeats. Automatic detection of arrhythmia is possible by analyzing a patient's abnormal heartbeats and has become a major research area in recent years, as the manual examination of heart activity is time-consuming and prone to errors. Nowadays, the deployment of artificial intelligence (AI) - based algorithms to predict abnormal heartbeats categorized into five classes namely, non-ectopic (N), supra ventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beats (Q) has drawn more attention in detecting arrhythmias. The use of intuitive hand-crafted features with shallow feature learning architectures is one of the key drawbacks of machine learning (ML) techniques. So, we present a novel deep neural network heartbeat classifier to extract and classify the heartbeat signals. The novel one-dimensional convolution neural network (1D CNN) model is developed by modifying the LENET architecture for the classification of heat beats (MIT-BIH Arrhythmia Database) and has attained an accuracy of 97.37%. This model's performance is also enhanced by the implementation of smote oversampling technique and gained an accuracy of 98.41%. Finally, the proposed model's performance is compared with other pre-existing models and various oversampling methods are deployed for analysis.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194734","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

Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud 混合云存储系统，增强多层密码系统，保证云环境下重复数据删除的安全性

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.11.001

Nagappan Mageshkumar , J. Swapna , A. Pandiaraj , R. Rajakumar , Moez Krichen , Vinayakumar Ravi

{"title":"Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud","authors":"Nagappan Mageshkumar , J. Swapna , A. Pandiaraj , R. Rajakumar , Moez Krichen , Vinayakumar Ravi","doi":"10.1016/j.ijin.2023.11.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.001","url":null,"abstract":"<div><p>Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 301-309"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000295/pdfft?md5=87683c34f716c34cf5f33cc644cd5155&pid=1-s2.0-S2666603023000295-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138454137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent prediction method for power generation based on deep learning and cloud computing in big data networks 基于深度学习和云计算的大数据网络发电智能预测方法

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.08.004

Zhaolong Zhou

{"title":"Intelligent prediction method for power generation based on deep learning and cloud computing in big data networks","authors":"Zhaolong Zhou","doi":"10.1016/j.ijin.2023.08.004","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.08.004","url":null,"abstract":"<div><p>This paper aims to elevate the precision and efficiency of prevailing photovoltaic prediction algorithms by integrating deep learning and cloud computing techniques. The emphasis lies in leveraging measured solar power generation data to simulate the model's predictive capabilities and determine optimal parameters. The study employs a hybrid approach, combining a multilayer perceptron-deep belief network (MLP-DBN) algorithm, and contrasts it with other methods like support vector machine (SVM), long short-term memory (LSTM), multilayer perception (MLP), and deep belief networks (DBN). Assessment of model performance encompasses root-mean-square deviation, mean absolute error and the decision coefficient metrics. Empirical results highlight the superiority of the MLP-DBN technique, showcasing reductions in root mean square error by 2.20%, 1.64%, 2.09%, and 4.83%, and mean absolute error by 0.67%, 0.11%, 1.12%, and 1.30%, respectively. The coefficient of determination (R2) exhibits notable increments of 2.96%, 2.05%, 2.77%, and 8.64%. These strides underscore substantial advancements in prediction accuracy and error mitigation. The findings underscore the efficacy of the proposed hybrid model in ameliorating existing photovoltaic forecast algorithms, effectively addressing their limitations, including inadequate accuracy and performance.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 224-230"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194619","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

Prediction of floods using improved PCA with one-dimensional convolutional neural network 基于一维卷积神经网络的改进PCA洪水预报

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.05.004

Tegil J. John, R. Nagaraj

{"title":"Prediction of floods using improved PCA with one-dimensional convolutional neural network","authors":"Tegil J. John, R. Nagaraj","doi":"10.1016/j.ijin.2023.05.004","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.05.004","url":null,"abstract":"<div><p>Forecasting floods have always been a difficult task due to the complexity of the available data. Machine learning techniques have been widely used to predict floods based on precipitation, humidity, temperature, water velocity, and level variables. However, most prior studies have examined the monthly rainfall intensity to determine the likelihood of flooding. As a result, a state's daily and monthly rainfall intensity has been used to train deep-learning models to predict floods. In addition, feature reduction approaches are critical for dealing with data of a large dimensionality and improving classification accuracy. This article utilizes improved Principal Component Analysis (i-PCA), a linear unsupervised statistical transformation, as a feature reduction procedure. A 1D-Convolutional Neural Network (CNN) model forecasts the flood based on the reduced features. The experiments are based on a dataset of daily and monthly rainfall data collected from 1901 to 2021 for Kerala state. Qualitative analysis is performed using precision, accuracy, recall and F1-score parameters. The experiment analysis proves that the proposed algorithm attained 94.24% accuracy, and existing techniques achieved 86% of accuracy performance. The reason is that the proposed model uses the improved PCA for the feature reduction technique.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 122-129"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194626","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

Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison 使用机器学习技术和数字孪生的太阳辐照度预测模型：一个案例研究和比较

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.04.001

Neha Sehrawat , Sahil Vashisht , Amritpal Singh

{"title":"Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison","authors":"Neha Sehrawat , Sahil Vashisht , Amritpal Singh","doi":"10.1016/j.ijin.2023.04.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.04.001","url":null,"abstract":"<div><p>The ever-increasing demand for energy and power consumption due to population growth, economic expansion, and evolving consumer choices has led to the need for renewable energy sources. Traditional energy sources such as coal, oil, and gas have contributed to global pollution and have adverse effects on human health. As a result, the use of renewable energy for power generation has increased tremendously. One such area of research is solar irradiation prediction, which utilizes Artificial Intelligence and Machine Learning techniques. With the use of real-time predicted data, the digital twins are intended to add value to the organization by identifying and preventing problems, predicting performance, and improving operations. This paper provides an overview of various learning methods used for predicting irradiance and presents a new ensemble solar irradiance forecasting model that combines eight machine learning models to ensure model diversity. The model's most critical factors for predicting irradiance include temperature, cloudiness index, relative humidity, and day of the week. To conduct a comprehensive analysis, the proposed 8-Stacking Regression Cross Validation (8 STR-CV) model was tested using data from three different climatic zones in India. The model's high accuracy scores of 98.8% for Visakhapatnam, 98% for Nagpur, and 97.8% for the mountainous region make it a valuable tool for future prediction in various sectors, including power generation and utilization planning.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 90-102"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194631","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: 3

Intelligent personalized content recommendations based on neural networks 基于神经网络的智能个性化内容推荐

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.09.001

HeQiang Zhou

{"title":"Intelligent personalized content recommendations based on neural networks","authors":"HeQiang Zhou","doi":"10.1016/j.ijin.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.09.001","url":null,"abstract":"<div><p>To effectively assist users in discovering content tailored to their specific interests, this research aims to create an intelligent content recommendation system. The inadequacy of conventional recommendation models, which depend uniquely on historical reading data, becomes evident in their limited capacity to meet contemporary users' diverse and ever-changing preferences within the information. The proposed architecture makes the most of the advancements in deep learning technology. It integrates the self-attention mechanism, allowing for precise calibration of the significance attributed to each feature within the news data. The proposed multilevel data classification network enables a more refined and personalized knowledge of users' preferences and the array of content information attributes while incorporating the users' unique characteristics. The proposed model achieved an accuracy rate of 85.2%, a recall rate of 83.7%, an F1 score of 84.3%, and an Area Under the Curve (AUC) of 84.5%. By developing a multilevel, intelligent, personalized content recommendation network, the research attempts to introduce a solution that effectively provides users' preferences, thereby enriching their experience in discovering relevant information within the modern digital system.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 231-239"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194718","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

Covertvasion: Depicting threats through covert channels based novel evasive attacks in android 隐蔽入侵：通过隐蔽渠道描绘基于新颖规避攻击的安卓威胁

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.11.006

Sunil Gautam , Ketaki Pattani , Mohd Zuhair , Mamoon Rashid , Nazir Ahmad

{"title":"Covertvasion: Depicting threats through covert channels based novel evasive attacks in android","authors":"Sunil Gautam , Ketaki Pattani , Mohd Zuhair , Mamoon Rashid , Nazir Ahmad","doi":"10.1016/j.ijin.2023.11.006","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.006","url":null,"abstract":"<div><p>Privacy and security issues concerning mobile devices have substantial consequences for individuals, groups, governments, and businesses. The Android operating system bolsters smartphone data protection by imposing restrictions on app behavior. Nevertheless, attackers conduct systematic resource analyses and divert privacy-sensitive information from plain view. They employ evasive mechanisms to evade system monitoring and create an illusion of benign and non-sensitive communication. Furthermore, covert channels amplify the impact of these malicious activities by facilitating information transfer through non-standard methods. The purpose of this research is to shed light on these novel threats targeting Android systems. The study delves into security and privacy attacks that compromise sensitive user information. The methodology leverages evasion concepts and employs sound-specific covert channel communication, particularly ultrasonic channels. This research work introduces novel evasive attacks, namely Prime-Composite Evasive Information Invasion (PCEII) and File-lock-based Evasive Information Invasion (FEII), both relying on covert channel communication. These unique variants of attacks successfully evade user data within a few milliseconds for both noisy as well as non-noisy environments and do not show any signs of detection by antivirus mechanisms like Anti-Virus Guard (AVG), 360 security, etc. and state-of-the-art tools such as TaintDroid, MockDroid and others. The paper not only assesses their impact on the privacy and security of information but also introduces avenues for their detection and mitigation.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 337-348"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000349/pdfft?md5=4613738e91ae35e1a7c1f702498bd0ca&pid=1-s2.0-S2666603023000349-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proposed artificial intelligence algorithm and deep learning techniques for development of higher education 为高等教育发展提出的人工智能算法和深度学习技术

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.03.002

Amin Al Ka'bi

{"title":"Proposed artificial intelligence algorithm and deep learning techniques for development of higher education","authors":"Amin Al Ka'bi","doi":"10.1016/j.ijin.2023.03.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.03.002","url":null,"abstract":"<div><p>Artificial intelligence (AI) has been increasingly impacting various aspects of our daily lives, including education. With the rise of digital technologies, higher education has also been experiencing a transformation, and AI has been playing a crucial role in this transformation. The application of AI in higher education has been rapidly increasing, with a focus on improving student engagement, increasing efficiency, and enhancing the learning experience. The use of AI in higher education is not without its challenges and ethical considerations. One of the biggest challenges is ensuring the accuracy and fairness of AI algorithms, as well as avoiding potential biases. In addition, there are concerns about the privacy of student data, as well as the potential for AI to replace human instructors and support staff. Another challenge is ensuring that AI is used in a way that supports the overall goals of higher education, such as promoting critical thinking and creativity, rather than just being used as a tool for automating tasks and increasing efficiency. In this article, we will discuss the various ways in which AI is being applied in higher education where a proposed model for improving the cognitive capability of students is proposed and compared to other existing algorithms. It will be shown that the proposed model shows better performance compared to other models.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 68-73"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194632","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: 4