International Journal of Information Technology最新文献

{"title":"Next generation hybrid based flying squirrel search optimization approach for cubic boost converter used in solar photovoltaic system","authors":"Veerabhadra Jadhav, S. Nagaraja Rao","doi":"10.1007/s41870-024-02132-1","DOIUrl":"https://doi.org/10.1007/s41870-024-02132-1","url":null,"abstract":"<p>Renewable energy sources (RES) reveal potential for the near future since they are sustainable and generate clean energy. Currently grid-connected solar photovoltaic (SPV) systems are becoming increasingly significant in meeting energy demand and contributing to clean energy production. The power electronic converter (PEC) plays a significant role in conversion, regulating and controlling the flow of power from RES. This research primarily focuses on high-gain cubic boost converter (HG-CBC) and simulated with the help of MATLAB/Simulink. The results are evaluated and contrasted with both traditional and other high-gain boost converters, focusing on the boost factor (B) and the total part count. It is essential to integrate SPV modules with MPPT system to capture the peak power available under varying temperature (T) and solar irradiation (G) levels. This research introduces a new hybrid based flying squirrel search optimization (FSSO) approach combined with Perturb &amp; Observe (P&amp;O) MPPT approach which allows for faster MPP, lesser oscillations at the output and high accuracy with less convergence time in contrast with P&amp;O and FSSO MPPT. The proposed hybrid based HFSSO with P&amp;O MPPT with HG-CBC exhibits low output voltage ripples of 0.039 % and a contrast lower rising time of 0.264 s and settled at 0.6 s.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184671","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}

{"title":"A novel graph convolutional networks model for an intelligent network traffic analysis and classification","authors":"Olusola Olabanjo, Ashiribo Wusu, Edwin Aigbokhan, Olufemi Olabanjo, Oseni Afisi, Boluwaji Akinnuwesi","doi":"10.1007/s41870-024-02032-4","DOIUrl":"https://doi.org/10.1007/s41870-024-02032-4","url":null,"abstract":"<p>Network security in the midst of evolving and complex cyber-attacks is a growing concern. As the complexity of network architectures grows, so does the need for advanced methods in network traffic analysis and classification. This study explores the application of a novel Graph Convolutional Networks (GCNs) to address the challenges associated with intelligent network traffic analysis. The network interactions are modeled as a graph, where nodes represent devices or IP addresses, and edges capture the communication channels between them. In this work, dataset which contains packet information of some network devices was obtained from an online repository. The data was preprocessed, normalized and label-encoded. Seven baseline models, including Feed Forward Network (FFN) were developed as reference to the proposed GCN. The parameters were tuned to optimize the performance and the dataset was split into average train-test to avoid overfitting. Two convolutional fully-connected layers were used also as more could cause oversmoothing. Performance of the novel GCN was compared with the reference models. The improved GCN model gave classification accuracy of 94.3% compared to classical GCN with 92.5% and FFN with 88%. Results also showed that the enhanced GCN proposed in this study outperformed the classical GCN and FFN in precision, recall, F1 score and area under curve metrics. Through the utilization of a GCN architecture and proposed enhancements, the proposed model demonstrates notable effectiveness in accurately classifying diverse types of network traffic. This research showed the efficacy of GCNs in intelligent network traffic analysis, offering a promising approach to augmenting cybersecurity efforts in an evolving digital landscape.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184675","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}

{"title":"Offline handwritten signature authentication using Graph Neural Network methods","authors":"Ali Badie, Hedieh Sajedi","doi":"10.1007/s41870-024-02149-6","DOIUrl":"https://doi.org/10.1007/s41870-024-02149-6","url":null,"abstract":"<p>Due to their uniqueness and simplicity, handwritten signatures are used as a behavioral biometric feature to identify and authenticate individuals. Due to the increase in the criminal activity of forgers in forging signatures, organizations are forced to use computer systems to verify the authenticity of signatures. For this reason, offline signature verification system is widely used in most organizations. Despite the abundance of research conducted on signature verification, it is difficult to distinguish real and forged signature samples due to the lack of information in the signing process. On the other hand, the small number of training samples is a challenge for offline signature recognition systems. In recent years, to improve these problems, systems based on machine learning and deep learning methods have been presented. In this paper, we have proposed a graph neural network-based architecture for offline signature verification. In this work, the features in the signature images, which are the pixels that make up the signature, are extracted by the SIFT algorithm and sent to the graph-based neural network as a graph structure. After training the network, the data of the test samples are classified into one of two classes, genuine or forged. The proposed model was evaluated on two datasets, MCYT-75 and UTSig, and Accuracy (Acc), Average Error Rate (AER), False Acceptance Rate (FAR) and False Positive Rate (FPR) were considered as performance measures. In this model, the values of Acc, AER, FAR and FPR for the MCYT-75 data set are equal to 1,0, 0, and 0, respectively, and for the UTSig database, these values are equal to 0.092, 0.007, 0.014 and 0.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224095","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}

{"title":"LSTM and BERT based transformers models for cyber threat intelligence for intent identification of social media platforms exploitation from darknet forums","authors":"Kanti Singh Sangher, Archana Singh, Hari Mohan Pandey","doi":"10.1007/s41870-024-02077-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02077-5","url":null,"abstract":"<p>Cybercriminals, terrorists, political activists, whistleblowers, and others are drawn to the darknet market and its use for illicit purposes. Various methods are employed to identify the people who are behind these identities and websites. Since DNMs are more recent than other platforms, there are more unexplored research possibilities in this field. Research has been done to identify the buying and selling of products connected to hacking from Darknet Marketplaces, the promotion of cyber threats in hacker’s forums and DNMs, and the supply chain elements of content related to cyber threats. The proposed research covers one of the most promising research areas: darknet markets and social media platforms exploitation tools and strategies. The research uses 6 DNMs publicly available data and then identified the most popular social media platform and intent of discussion based on the interaction available in form of the user remarks and comments. The research caters the social media platform and cybercrimes or threats associated to them, by help of the machine learning algorithms Logistic Regression, RandomForestClassifier, GradientBoostingClassifier, KNeighborsClassifier, XGBClassifier, Voting Classifier and Deep Learning based model LSTM and Transformer based Model used. In existing research, natural language processing techniques were employed to identify the kinds of commodities exchanged in these markets, while machine learning approaches were utilized to classify product descriptions.In proposed research work advanced and lighter version of BERT and LSTM model used yielding accuracy of 90.12% and 91.35% respectively. LSTM performed best to extract multiclass classification of actual intension of social media usage by intelligent analysis on hackers’ discussions. Strategies on social media platforms such as Facebook, twitter, Instagram, Snapchat to exploit them using darknet platforms also explored. This paper contributes on cyber threat intelligence that leverages social media applications to work proactively to save their assets based on the threats identified in the Darknet.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184635","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}

{"title":"Knowledge distillation-based approach for object detection in thermal images during adverse weather conditions","authors":"Ritika Pahwa, Shruti Yadav, Saumya, Ravinder Megavath","doi":"10.1007/s41870-024-02107-2","DOIUrl":"https://doi.org/10.1007/s41870-024-02107-2","url":null,"abstract":"<p>In today’s technology landscape, systems must adapt to diverse conditions to be practically useful. Thermal imaging’s intersection with adverse weather presents a challenge for existing heavy networks designed for RGB images. This research addresses this gap by using knowledge distillation to optimise networks for thermal imaging in challenging weather. Current networks struggle with interpreting thermal images effectively in adverse conditions like fog or rain. Through knowledge distillation, our work aims to enhance these networks, ensuring compatibility and efficiency with thermal imaging. This effort holds promise for enhancing object detection in thermal images during adverse weather, benefiting surveillance systems, improving safety in self-driving vehicles under harsh conditions, and aiding search and rescue operations with limited visibility. This research doesn’t just refine networks; it empowers technology to excel in adverse conditions, promising practical applications that enhance safety, efficiency, and reliability across various technological domains.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"157 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184636","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}

{"title":"Cluster-head selection in WSNs using modified MADM approach by considering conflicting parameters for IoT applications","authors":"Lekhraj, Raushan Kumar Singh, Sachin Upadhyay, Vatsya Tiwari, Sanjiv Kumar Singh","doi":"10.1007/s41870-024-02133-0","DOIUrl":"https://doi.org/10.1007/s41870-024-02133-0","url":null,"abstract":"<p>Internet of Things (IoT) devices with limited energy and storage resources can access sensing services from wireless sensor networks (WSNs), which are collections of specialized transducers. Power consumption becomes one of the most important design considerations in WSN because battery replacement or recharge in sensor nodes is almost impossible. For the energy-constrained network, clustering algorithms are crucial for power conservation. By carefully balancing the network’s demand, a cluster head (CH) can lower energy usage and extend lifespan. The primary topic of this study is an effective CH election mechanism that alternates the CH position among nodes with higher energy levels than the others. In order to accomplish balanced load clustering in WSN, the method in this study takes into account a total of seven such factors and coordinates to select the optimal CHs from among them. To choose the set of CHs that can most effectively meet the coordination criterion from the available possibilities, modified MADM techniques are used. The improved version performs better, according to simulation analysis.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184633","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}

{"title":"Data augmentation and generative machine learning on the cloud platform","authors":"Piyush Vyas, Kaushik Muthusamy Ragothaman, Akhilesh Chauhan, Bhaskar Rimal","doi":"10.1007/s41870-024-02104-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02104-5","url":null,"abstract":"<p>This paper aims to explore the image data augmentation application on the cloud platform utilizing state-of-the-art generative machine learning techniques. This paper further highlights these techniques’ significance in addressing the challenge of data generation and emphasizes the need for further research in this area. This research adopts an in-depth exploration approach to examine the burgeoning domain of generative machine learning techniques. It discusses the evolution of these techniques and their integration with cloud services powered by Graphical Processing Unit (GPU)-enabled computational engines. Practical experimentation involving Modified National Institute of Standards and Technology (MNIST) data is conducted to showcase the capabilities of generative models, with a focus on the core Generative Adversarial Network (GAN). The findings reveal the potential of generative machine learning techniques in generating new data images, as demonstrated through practical experimentation with MNIST data. It also highlights the ongoing evolution of these techniques and their challenges, particularly in terms of computational requirements and integration with cloud computing services. This research originally contributes to the existing literature by providing insights into recent advancements and challenges in GANs and their synergies with cloud computing. It presents results from experimentation and emphasizes the importance of cost-effective development environments for implementing generative machine learning techniques.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942768","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}

{"title":"Enhancing mobility: strategies for integrated public transportation in Jakarta’s metropolitan area","authors":"Syafruddin","doi":"10.1007/s41870-024-02112-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02112-5","url":null,"abstract":"<p>For some people in the region, Jakarta is considered a business and economic center so many people come there. Every year the number of people coming to Jakarta increases, which makes Jakarta increasingly dense. This condition inevitably makes Jakarta one of the most congested cities, not only in Indonesia but also in the world. The high cost of living and housing ultimately makes some residents choose to live in supporting areas such as Bogor, Depok, Tangerang, or Bekasi, even though their main activities are in Jakarta. On the one hand, the relationship between buffer areas and the capital city has made Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek) agglomerated areas that are mutually dependent on each other. On the other hand, this condition creates complicated problems in the transportation sector. This is because more people use private vehicles for transportation to carry out daily activities. The majority of trips are made by private vehicles, causing traffic jams. Not only in Jakarta, traffic jams also occur in the areas where residents come to the capital. Therefore, the government is trying to create an integrated p in Jabodetabek will be much better. Public transportation (PT) needs to account for 72.8% of all people’s movements. This article aims to analyze the implementation of an integrated PT system. The target is that by 2029 transportation and system policies in Jabodetabek. In 2018, the government introduced presidential regulation number 55, addressing the Jabodetabek Transportation Master Plan. This article will provide policy implications in the form of transportation development that pays attention to integrated transportation systems, road-based transportation systems, integration of transportation and spatial planning, engineering management and traffic supervision, transportation safety and security, infrastructure networks, rail-based transportation systems, environmentally friendly transportation, and financing systems.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942766","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}

{"title":"Opposition-based optimized max pooled 3D convolutional features for action video retrieval","authors":"Alina Banerjee, Ravinder Megavath, Ela Kumar","doi":"10.1007/s41870-024-02102-7","DOIUrl":"https://doi.org/10.1007/s41870-024-02102-7","url":null,"abstract":"<p>Key frame selection serves as a c bridge between raw video data and meaningful retrieval results. Effective key frame selection enhances the performance of content-based video retrieval systems by reducing computational complexity, improving search accuracy, and enabling faster browsing through large video databases. Additionally, fixed keyframe sampling techniques do not address information optimization, which might lead to information redundancy or loss. For effective video retrieval, a keyframe selection method based on opposition-based learning has been developed. The outcomes show that the method performs better than numerous benchmark sampling strategies.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942774","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}

{"title":"Multi-level glowworm swarm convolution neural networks for abnormal event detection in online surveillance video","authors":"M. Koteswara Rao, P. M. Ashok Kumar","doi":"10.1007/s41870-024-02134-z","DOIUrl":"https://doi.org/10.1007/s41870-024-02134-z","url":null,"abstract":"<p>A surveillance camera is one of the most important tools for observing people's movements and stopping unauthorized or unplanned activity. Security management experts now significantly rely on video surveillance to combat crime and avert incidents that have a detrimental influence on human civilization. To monitor public activities, the installation of numerous surveillance cameras has drastically increased in both the public and private sectors. Security may be ensured most effectively through video surveillance. Installing a surveillance camera merely provides security personnel with the recorded video. However, integrating intelligent technology to analyze the videos is the only way to spot irregular actions. As a result, the goal of this study is to construct an Intelligent Video Analytics Model (IVAM), also known as a Human Object Detection (HOD) approach, for analyzing and spotting unusual human activity and abundant objects in videos. The proposed IVAM is designed based on Multi-level glowworm swarm convolution neural networks (ML-GSCNN). The proposed approach consists of two stages namely, frame conversion, and abnormal event detection. The captured video is first divided into segments, and then each segment is changed into a frame. After that, abnormal event detection is performed. For abnormal event detection, a novel ML-GSCNN is designed. Here, the hyper-parameter of CNN and the architecture of CNN both are optimized by the glowworm swarm optimization (GSO) algorithm to improve the detection accuracy. The experimental results show that the proposed approach attained better results compared to existing works.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"107 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942769","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}