User Information Research Articles

Artifical Intelligence-Based Smart Security System Using Internet of Things for Smart Home Applications

This study presents the design and development of an AI-based Smart Security System leveraging IoT technology for smart home applications. This research focuses on exploring and evaluating various artificial intelligence (AI) and Internet of Things (IoT) options, particularly in video processing and smart home security. The system is structured around key components: IoT technology elements, software management of IoT interactions, AI-driven video processing, and user information delivery methods. Each component’s selection is based on a comparative analysis of alternative approaches, emphasizing the advantages of the chosen solutions. This study provides an in-depth discussion of the theoretical framework and implementation strategies used to integrate these technologies into the security system. Results from the system’s deployment and testing are analyzed, highlighting the system’s performance and the challenges faced during integration. This study also addresses how these challenges were mitigated through specific adaptations. Finally, potential future enhancements are suggested to further improve the system, including recommendations on how these upgrades could advance the functionality and effectiveness of AI-based Smart Security Systems in smart home applications.

ConvSeq-MF: Convo-Sequential Matrix Factorization for recommender system

In the domain of recommendation, the fusion of user historical preferences and item features has emerged as a promising field to enhance recommendation accuracy and mitigate the sparsity problem. This research presents a novel approach to recommendation systems by integrating both user and item content auxilary information. By harnessing the rich contextual data associated with users and items, our hybrid aproach ConvSeq-MF endeavors to provide more personalized and accurate recommendations. As the name indivates, it is based on a matrix factorization approach driven by joint venture of semantic and contextual information of both user’s and item’s. Series of experimentation on three real-world datasets shows the suggested methodology’s practicality and efficacy over otther baselines. To the best of our knowledge, the data demonstrate that the suggested model works better than other state of the art frameworks.

Asatyajaal Anveshak

The Astayajaal Anveshak Project is a web application and browser extension that aims to boost online safety and address the growing threats of cyber- attacks that puts sensitive user information at risk. This project groups websites into four types based on their web addresses: safe, phishing, defaced, and malware-infected by Uniform Resource Locator(URL) detection and categorization. Along with the use of browser extension for real time website detection and categorization. It pops up a message when you visit a site telling you how safe it is. The system also has a control panel for admins to track and maintain prediction statistics known as centralized admin dashboard. This panel has a pie chart that breaks down the types of websites people visit helping admins keep tabs on safety trends. It also include user awareness module in the form of quiz to teach people about online dangers.

Phishing Website Detection: An In‐Depth Investigation of Feature Selection and Deep Learning

ABSTRACTCloud and fog computing technologies benefit from integrating AI‐driven phishing detection as it enhances security, scalability, real‐time reaction, and privacy. Nowadays, there is a noticeable rise in illegal activity taking place online. One of the illicit cybersecurity practices is phishing, in which hackers trick consumers by pretending to be authentic websites and spoofing them to obtain sensitive user information. Phishing attacks, regrettably, have increased dramatically in recent years, according to research. Machine learning (ML) and deep learning (DL) techniques have shown encouraging progress in thwarting these attacks. Consequently, we employed DL and ML techniques to identify phishing websites in this study. This article presents four scenarios in both ML and DL models. Two are proposed in ML, while the others are employed in DL. The outcomes of four scenarios were contrasted to determine which algorithm performed better at distinguishing between legal and illicit websites. Many popular ML techniques were used, including K‐nearest neighbour, random forest (RF), decision trees, and SVMs. PCA and Importance Features are implemented in both ML scenarios to find the best features. RF successfully reached an accuracy of 97.82% using the Importance Feature technique. However, the PCA method failed to improve the performance of ML algorithms. As a result of ML‐based scenarios, 98 features are selected for the final deep learning scenarios. In DL‐based scenarios, algorithm architectures are essential to avoid overfitting and bias due to various hyperparameters. Thus, in the third scenario, our aim focuses on DL architecture design. Multilayer perceptron and convolutional neural networks (CNNs) are employed to detect phishing websites. Finally, our proposed 1D CNN model, using stratified k‐fold cross‐validation, outperformed the classical ML algorithm, achieving 98.94% accuracy and 0.99 AUC‐ROC score in detecting phishing websites.

Implementation of an information system for tuberculosis in healthcare facilities in Indonesia: evaluation of its effectiveness and challenges

BackgroundTuberculosis (TB) surveillance in Indonesia is currently supported by the recording and reporting technology Tuberculosis Information System (Sistem Informasi Tuberkulosis [SITB]). SITB is used by all health workers responsible for TB programs in health facilities or hospitals throughout Indonesia. The satisfaction and ease of use of SITB affect the speed and accuracy of TB case reporting. Therefore, evaluating the information system’s quality is crucial. SITB utilization is comprehensively assessed using the End-User Computing Satisfaction (EUCS) method, which considers the dimensions of content, accuracy, timeliness, and ease of use. Furthermore, the effectiveness of the user interface using heuristic evaluation methods is critical. The primary objective of this study is to conduct a comprehensive evaluation of the System for Information on Tuberculosis (SITB) focusing on its usability and user satisfaction.MethodsThis study employed a multi-method design approach. First, a heuristic evaluation was conducted by three expert user experience designers. Second, a cross-sectional survey of 115 SITB users was carried out across all health facilities in the Special Region of Yogyakarta, Indonesia, for reporting TB cases, using the EUCS method. Finally, in-depth interviews were conducted in 10 health facilities, focusing on identifying specific attributes that needed improvement to enhance user satisfaction.ResultsThe evaluation indicated that user convenience and timeliness require improvement, as well as the match between the system and the real world and error prevention, as shown in the heuristic evaluation of the SITB user interface. These findings were confirmed through interviews that identified the need for user manuals, server repairs, and information and SITB forms that are suitable for TB management in the field. The overarching assessment of the SITB reveals persistent challenges in data entry, attributed to the functionality of the application, thereby potentially impacting user satisfaction.ConclusionsSITB application requires “major” improvements to make recording, reporting, and tracing activities more efficient and precise. Moreover, the integration of existing information systems in healthcare facilities is essential to support the tuberculosis surveillance reporting system in Indonesia.

Privacy-preserving attribute-based access control using homomorphic encryption

Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.

The unfolding of geopolitical tensions on social networks: a social network analysis of Twitter and Reddit conversations

PurposeSocial networks can not only mobilize individuals for collective action but also pose risks, potentially leading to political challenges and societal unrest. Information consumption varies across platforms, with platform characteristics influencing user interactions and information sharing; yet this has received limited attention in scholarly literature. Acknowledging platform-specific differences, this paper seeks to enhance our understanding of the mechanisms driving information diffusion on social networks in the context of geopolitical tensions.Design/methodology/approachThe structural communication features on Twitter and Reddit are explored using schema theory and the concept of social media platform schema. Comparisons are drawn with social network analysis and content analysis of communication dynamics surrounding geopolitical tensions in India–Qatar relations, followed by the context of geopolitical tensions between India and Pakistan.FindingsThe results illustrate how content-based connections on Reddit foster closer ties within subreddits but less connectivity between them, contrasting with Twitter’s profile-based connections. These distinct characteristics lead to varied information diffusion patterns and shape the diversity of opinions, influencing community structures and affecting the emotional tenor of discourse.Originality/valueSocial networks can potentially influence geopolitical events, but focusing on one platform overlooks differences in how information spreads and the influence each platform holds. Recognizing this, our comparative analysis of social networks’ structural attributes highlights their crucial roles in shaping user engagement and information diffusion. It lends theoretical support to the notion of social media platform schema with empirical insights into how users’ perceptions of these schemas impact thematic and emotional differences in platform discourse related to geopolitical tensions.

On the issue of the use of artificial intelligence technologies by financial market entities and ensuring the security of their personal and corporate data

The current study analyzes the issues of the accelerated development of artificial intelligence (AI) in the Russian Federation, the use of artificial intelligence technologies by market participants, including financial ones, and ensuring the security of their personal and corporate data, in the context of the rapid progress of AI technologies, contributing to the efficiency of technological solutions through the use of various methods and algorithms. Development in this area is supported by government agencies, scientific institutions, government organizations, and leading companies, which portends a significant expansion of the market by 2030. Artificial intelligence offers many opportunities, but it also has negative aspects. One such aspect is the collection of personal information of users, which is necessary in order to maintain, improve the performance of AI. This can happen either within the law or through illegal actions by digital criminals. Collecting all sorts of data allows algorithms to learn, adapt to society's needs, offer personalized recommendations, improve app interactions, much more. But misuse of these practices leads to serious privacy threats. The responsibility for protecting the information received lies with organizations, including financial ones, which must comply with security measures and proper protection of personal information. However, users themselves are sometimes insufficiently vigilant and cautious in handling their data, thereby increasing the risk of confidential information leakage. Cases of increasing digital crimes confirm that AI creates favorable conditions for various manipulations by cybercriminals, which negatively affects the economic security of the country and its citizens. The article discusses the current problems associated with the introduction of artificial intelligence in all spheres of human life, provides statistics on the number of court cases related to artificial intelligence, concludes that it is necessary to introduce a multi-level set of measures, including not only the general regulation of artificial intelligence, providing basic principles and approaches, but there are also self-regulation mechanisms that will allow market participants to adapt to rapidly changing conditions.

Information System Benefits Improvement Model, Information Quality Through User Satisfaction, Information Quality and Organization At Hospital X

Hospital Information System aims to provide accurate, timely, and relevant information to support management functions and decision-making in health services in hospitals. This study examines the influence of user satisfaction, information quality, and organizational support on the benefits obtained from SIRS, both directly and indirectly. The approach used in this study is quantitative with the explanatory causality method, and the analysis technique in this study uses Structural Equation Modeling (SEM). The respondents of the study were 198 professional care providers (PPA) who were directly involved in the use of SIRS at the Sultan Agung Islamic Hospital, Banjarbaru. The results of this study indicate that the factors of user satisfaction, information quality, and organizational support have a major influence on the benefits both directly and indirectly received from the use of this system, with a determination coefficient of 84.8%. This means that more than 80% of the benefits obtained by the hospital in using the hospital information system are determined by how satisfied the user is with the system used. This indicates a good relationship between the four factors in increasing the benefits of the hospital information system at RSI Sultan Agung Banjarbaru. Therefore, hospitals are advised not to only focus on developing one aspect, but also to maximize the four aspects in order to provide more optimal results.

Vulnerabilities and Encryption Applications of JWT-Based Authentication Methods

Most modern web applications send and receive data using the HTTP (HyperText Transfer Protocol) transmission method. HTTP has the characteristic of being non-connected. Due to this characteristic, the client must inform the server every time that he or she is an authenticated user. There are currently several authentication methods for this, such as cookies, session cookies, and tokens. Currently, a JSON-formatted token called JWT is being widely used. JWT is used in domains such as QR check-ins and social logins due to its fast and simple characteristics. However, JWT has a critical security vulnerability because it contains the user's information as it is in the token. If an attacker steals a token on the network, the user's information can be easily obtained by simply base64 decoding. The purpose of this study is to protect the user's information from the attacker by encrypting the payload part containing the user's information and the header part containing the hash information.

Health-Fots- A Latency Aware Fog Based IoT Environment and Efficient Monitoring of Body’s Vital Parameters in Smart Health Care Environment

Internet of Things (IoT) integrated with the disruptive technologies are becoming increasingly popular and they have extended their capabilities in all domains such as automotive, health care and automation. IoT is connecting the billions of devices and humans to bring the fruitful advantages to society. Since IoT devices are operated with the centralized cloud environment, pervasive and continuous monitoring of the user information can be facilitated. However, owing to the inherent characteristics of cloud, such as large end-to-end latency, larger bandwidth consumption, handling the larger volume of data from the IoT devices would be bottleneck for implementing the IoT for the smart health care system that aids for the treatment and diagnosis process. To address these issues, this research article proposes powerful paradigm, Heath-FoTs (Fog of things) which incorporates the fog devices where the data are processed and filtered near the IoT nodes which is useful for improving the quality of services. To further improve the speed of communication, distributed fogs are introduced between the IoT devices and Cloud to process the health care data and provides the optimal solution to tackle the latencies problems and bandwidth requirements. The complete experimentation is carried out using the NodeMCU and Raspberry Pi 3 Model in which the MQTT (Message Queuing Telemetry Transportation) protocol is used as the major communication protocol between the IoT and Fog Nodes. To evaluate the proposed model, performance metrics such as latency, throughput, and communication cost is measured and compared with the traditional environments. Results demonstrate the Health-FoTs environment has shown the promising performance with the 23% lesser latency, 32% higher throughputs and 25% less communication overhead than the traditional IoT infrastructure and proves its strong place for the high speed health care environment.

RIS-Aided coexistence in wireless networks using angular information

Owing to the ability of reconfigurable intelligent surfaces (RISs) to control the propagation environment in their vicinity, they have emerged as an appealing solution to enhance the performance of next-generation wireless networks. While the effectiveness of RISs with complete channel state information (CSI) is well-documented, their performance with limited user information remains less explored. This is particularly pronounced when an RIS interferes with nodes in a potentially non-cooperating network, making CSI acquisition challenging. This paper focuses on such scenarios, examining the interference suppression capabilities of an RIS for coexisting secondary and primary users (SUs and PUs) using angular information, achievable even with non-cooperating PUs. We address the coexistence challenge by employing semidefinite relaxation (SDR) and inner majorization minimization (iMM) for RISs with continuous phase control, and propose an enhanced SDR tailored for discrete RISs. Numerical results reveal both SDR and iMM require at least five bits of discrete phase control for moderate interference suppression. They also indicate that interference suppression weakens if the PUs are inside certain angular regions, a limitation that varies with the algorithm but can be mitigated by increasing RIS elements. Ultimately, the coexistence performance of the RIS with a non-cooperative network is validated through full-wave simulations.

AI-Powered GPT-Based University-Specific Chat Assistant: UniROBO

AI-powered chat applications are innovative solutions that facilitate user interaction and information access. These applications improve user experience by providing personalized and context-sensitive responses thanks to large language models and natural language processing techniques. This study examined the design and development process of UniRobo, an AI-powered chat application developed for Malatya Turgut Ozal University students and staff. UniRobo is an application that provides instant information on topics such as education, food menus, and campus events and offers personalized responses using large language models and natural language processing techniques. In the development process, based on user needs analysis, the mobile application was created with React Native, the back-end was created with Python and FastAPI, and the MongoDB database was integrated. Artificial intelligence capabilities were supported by OpenAI API and fine-tuning, thus adapting to university-specific content. Retrieval-Augmented Generation (RAG) architecture and Azure AI Search technology increased user satisfaction by providing more accurate and faster responses. As a result, UniRobo has made university life more accessible, providing users with access to fast and accurate information, and has demonstrated the potential of artificial intelligence-based solutions in the education sector.

Connected wine packaging in retail: quantifying customer interaction and preferences for wine information

PurposeThis research explores wine customer interaction with connected packaging in retail, offering insights for wine producers to enhance customer behavioural engagement strategies. The study aims to understand patterns of customer visual attention and information preferences in the context of connected packaging.Design/methodology/approachThe research adopts an empirics-first (EF) approach, including a two-study mixed-method research design. EF is particularly suited to exploring emerging customer behaviours in innovative technological contexts. Study One uses Google Analytics and Tag Manager to measure connected packaging users’ visual attention and behavioural engagement. Study Two uses maximum difference scaling to assess user preferences for wine attributes in retail.FindingsStudy One reveals distinct patterns of user behavioural engagement. Highly engaged users exhibit deeper interaction with product, brand and sustainability information, often revisiting content. First-time users focus on top-of-page content before disengaging. Study Two results corroborate the importance of product information (grape variety, wine style and region of origin), with highly involved wine consumers showing greater interest in connected packaging.Originality/valueThis research advances the understanding of connected packaging user interaction and information preferences, an area previously underexplored. It demonstrates the potential of connected packaging for enhancing ongoing customer behavioural engagement and providing additional product information. The study’s innovative EF approach, combining real-world behavioural data with preference analysis, supports and extends existing research on wine attribute preferences while demonstrating the potential of connected packaging as an ongoing value co-creation tool.

Architecture and Usage Information for a Course-Aligned, GPT-Powered Chatbot

Large Language Models (LLMs) offer impressive natural language understanding abilities and are suitable for the development of educational chatbots for engineering students. We present the architectural design of the Oliver chatbot, an LLM application for answering natural language queries over a corpus of course-aligned documents. Oliver is based on a Retrieval Augmented Generation system and is equipped with a user-friendly web interface. We present Oliver’s use-cases and demonstrate how logging data can be used for generating reports on student learning throughout a term. We support this using data obtained during the Fall of 2023, in which Oliver was deployed to a class of approximately 100 Mechatronics Engineering students.

Private Data Protection with Machine Unlearning in Contrastive Learning Networks

The security of AI models poses significant challenges, as sensitive user information can potentially be inferred from the models, leading to privacy breaches. To address this, machine unlearning methods aim to remove specific data from a trained model, effectively eliminating the training traces of those data. However, most existing approaches focus primarily on supervised learning scenarios, leaving the unlearning of contrastive learning models underexplored. This paper proposes a novel fine-tuning-based unlearning method tailored for contrastive learning models. The approach introduces a third-party dataset to ensure that the model’s outputs for forgotten data align with those of the third-party dataset, thereby removing identifiable training traces. A comprehensive loss function is designed, encompassing three objectives: preserving model accuracy, constraining gradients to make forgotten and third-party data indistinguishable, and reducing model confidence on the third-party dataset. The experimental results demonstrate the effectiveness of the proposed method. Membership inference attacks conducted before and after unlearning show that the forgotten data’s prediction distribution becomes indistinguishable from that of the third-party data, validating the success of the unlearning process. Moreover, the proposed method achieves this with minimal performance degradation, making it suitable for practical applications in privacy-preserving AI.

Construction of embedded online teaching platform based on AES-RSA encryption algorithm

With the development of economy, people's demand for high-quality and diversified educational resources increases. However, under the frequent occurrence of network attacks, data leakage and other events, network security and information security need to be solved urgently by the current online teaching platform. To this end, the research adopts Advanced Encryption Standard encryption algorithm and introduces Rivest-Shamir-Adleman to improve it, and applies it to construct an embedded online teaching platform, aiming to ensure user’s personal information security. Experimentally, the decryption speed of the Advanced Encryption Standard-Rivest-Shamir-Adleman encryption algorithm stays around 100 ms when the number of bytes is below 60. When file size increasing, the encryption time of the proposed algorithm increases minimally. Also, the key generation time of the algorithm is always less than 1500 ms, and the average time taken to defend the online teaching platform against brute force attacks can be up to 232.1 ms. In addition, the research algorithm has the best scalability, the platform obfuscation is better, and the platform always has the lowest ciphertext bit count. In conclusion, it can be seen that the embedded online teaching platform of the study has the best confidentiality of user information, which guarantees the confidentiality and integrity of learners' personal information and teaching resources. Strong encryption technology provides solid security for the application of online education platforms, allowing educational innovation to take place in a more secure and controlled environment.

Older Adults’ Disorientation in Smartphones: Exploring the Role of Spatial Ability and Quantifying Cognitive Similarity Between Perceived and Physical Information Structure

Older adults are prone to getting lost when switching between screens on smartphones. Navigating through screens can be demanding regarding the user’s spatial ability, which tends to decline with age. This study investigates the impact of information structure and spatial ability on older adults’ navigation in smartphone applications. It proposes a method of quantifying cognitive similarity between older adults’ perceived information structure and designers’ information structure used in applications. Researchers designed three different structured applications as prototypes, with low, medium, and high levels of netting, but with the same contents. Thirty older adults over 65 years of age and thirty younger adults were recruited to participate in the experiment, completing two types of navigation tasks. There were three main findings from our experiment. Firstly, the more reticulated the information structure, the longer the navigation task took the older adults to complete and the more errors were returned, which was the opposite of the results for younger adults. Second, spatial ability has no significant impact on the performance of the process page navigation task. However, it has a significant correlation with the cognitive similarity of older adults to a certain information structure. The higher the score of spatial memory span and pointing ability, the more accurate the cognition construction. Thirdly, the fitting degree of user mind and information structure can be predicted on the premise of specific information structure and given user spatial ability.

IMPLEMENTATION OF DIGITAL TWINS BASED ON GPT-3.5 FOR ENHANCING ENGLISH LANGUAGE LEARNING IN HIGHER EDUCATION INSTITUTIONS

This paper presents the development of an interactive web-based platform aimed at enhancing English language learning through the utilization of digital twins, powered by the GPT-3.5 model. The platform integrates three primary services essential for language learning: vocabulary acquisition, grammar checking, and conversational practice via simulated interlocutors, offering a dynamic, personalized learning experience. Detailed technical aspects of the system’s architecture are discussed, highlighting the implementation of the Model-View-Controller (MVC) framework, which ensures modularity and scalability. The platform’s backend is developed using C# for reliability and performance, while the frontend leverages HTML, CSS, JavaScript, and the Bootstrap framework to create a responsive, user-friendly interface that adapts to various screen sizes and devices. To support large volumes of user data, a Microsoft Azure SQL Database is employed for robust data management, enabling efficient storage and retrieval of user information, interaction histories, and progress logs. Integration with GPT-3.5 via OpenAI's API facilitates real-time query processing and response generation, making the platform a powerful tool. The platform uses advanced personalization algorithms to adjust learning content based on user preferences, progress, and interaction history. This personalized approach increases student engagement and promotes more effective learning outcomes by adapting content to individual needs. Security and ethical considerations are addressed through encryption protocols and authentication mechanisms. In addition, OpenAI’s built-in content filtering systems ensure that inappropriate or harmful content is blocked, safeguarding users while maintaining high ethical standards. Furthermore, a detailed cost calculation model for token usage is presented, which allows for precise tracking of operational costs, ensuring the platform’s long-term sustainability. By integrating cutting-edge technology with pedagogical principles, this platform demonstrates the potential to revolutionize English language learning in higher education institutions, making it more interactive, personalized, and effective.

Community Detection Framework Using Deep Learning in Social Media Analysis

Social media analysis aims to collect and analyze social media user information and communication content. When people communicate through messages, phone calls, emails, and social media platforms, they leave various records on their devices and the Internet, forming a huge social network. Community detection can help investigators analyze group leaders and community structure, which is significant to further crime control, identifying coordinated campaigns, and analyzing social network dynamics. This paper proposes the application of deep learning methods for community detection. Our main idea is to utilize social network topology and social network communication content to construct user features. The proposed end-to-end community detection framework is the implementation of Graph Convolution Network and can display the social network topology, locate the core members of the community, and show the connections between users. We evaluate our framework on the Enron email dataset. Experimental results indicate that our proposed model achieves a 1.1% higher modularity score than the unsupervised benchmark methods. We also concluded that the community detection framework should be able to analyze social networks, enabling investigators to reveal connections between people.