Ensure Data Security Research Articles

Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants

BackgroundThe variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.ResultsTo address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.ConclusionsIn summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC.

Research on Grid Multi-Source Survey Data Sharing Algorithm for Cross-Professional and Cross-Departmental Operations Collaboration

This paper addresses the problem of multi-source survey data sharing in power system engineering by proposing two improved methods: a survey data sharing method combined with differential privacy and a permission change method based on attribute encryption. The survey data sharing method integrated with differential privacy achieves effective cross-professional and cross-departmental data sharing while ensuring data security by introducing multi-discriminator architecture and dynamic noise adjustment. To reduce the computational and communication overhead when user permissions change during survey data sharing, the attribute encryption-based permission change method supports dynamic changes in user permissions. The effectiveness of the proposed methods has been validated through targeted experiments in different scenarios. The work in this thesis provides a new solution for dynamic sharing of survey data in power network engineering. It contributes to the digital transformation of power network projects.

Artificial Intelligence and Ethics in Healthcare: A Bibliometric Analysis

The rapid proliferation of artificial intelligence (AI) in healthcare services has underscored the importance of ethical considerations. This development highlights the need to examine ethical implications, debates, concerns, and thoughts from diverse and broad perspectives. In this context, the study focuses on the ethical dimensions of AI in the healthcare domain. AI is increasingly being used in various healthcare applications, but this usage brings along ethical challenges. The aim of the research is to identify themes, trends, and critical points related to AI ethics in healthcare. Through literature review and bibliometric analyses, it is observed that AI ethics research in healthcare revolves around fundamental concepts such as ethics, AI, machine learning, healthcare services, and privacy. Additionally, the leading countries, authors, and institutions in the field are examined. The intensity of collaboration and knowledge sharing in the literature is steadily increasing. In conclusion, considering the potential benefits and challenges of AI use in healthcare, addressing ethical issues, ensuring data security, and enhancing transparency in AI decision processes are crucial. The study aims to provide a deeper understanding of AI ethics topics in the existing literature and guide future research.

Blockchain-Based Vendor Management in IT: Challenges and Solutions

The rapid evolution of the IT sector has led to increasingly complex vendor management systems, necessitating innovative solutions to handle the multifaceted challenges associated with these systems. Traditional vendor management practices often struggle with issues related to transparency, security, inefficiencies in communication, and trustworthiness among vendors. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a compelling solution to these challenges. This research paper explores the application of blockchain technology in IT vendor management, focusing on its potential to address critical challenges and enhance the overall efficiency of vendor-related processes. The paper begins by outlining the inherent challenges in conventional vendor management systems, including difficulties in verifying vendor credentials, managing contracts, ensuring data security, and maintaining a reliable audit trail. These challenges often result in operational inefficiencies, increased costs, and potential risks related to vendor fraud or non-compliance. Blockchain technology, known for its secure, transparent, and decentralized nature, offers a transformative approach to these challenges. By providing a distributed ledger that records all transactions in a secure and immutable manner, blockchain can significantly enhance the transparency and security of vendor management processes. The paper examines how blockchain can be used to automate vendor verification, streamline contract management through smart contracts, and ensure data integrity across the vendor lifecycle.

Innovative methods for integrating translation memory and CAT tools: Enhancing intelligent support in human translation processes

This paper explores the innovative integration of Translation Memory (TM) and Computer-Assisted Translation (CAT) tools to enhance translation efficiency, consistency, and quality in multinational organizations. By adopting a user-centric interface design, modular system architecture, and cloud-based deployment, the integrated system addresses diverse translation needs while ensuring data security and privacy. Key features include real-time translation suggestions powered by machine learning algorithms, seamless access to TM databases, and real-time collaboration tools. The paper discusses implementation strategies, challenges, and solutions, highlighting the importance of user training and continuous improvement. A case study demonstrates significant improvements in translation speed, accuracy, and user satisfaction, underscoring the potential of advanced translation technologies to transform translation workflows. The findings provide valuable insights into best practices for successful implementation and optimization of TM and CAT tools in complex, large-scale environments.

Challenges, Barriers, and Facilitators in Telemedicine Implementation in India: A Scoping Review.

Telemedicine has revolutionized healthcare access by breaking geographical barriers and facilitating remote consultations. The eSanjeevani platform has been pivotal in India, especially during the COVID-19 pandemic. Despite its benefits, implementing telemedicine faces various challenges and barriers. This scoping review aims to identifythese challenges, barriers, and facilitators in the Indian context. This review follows the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines. A comprehensive literature search was conducted using PubMed and Google Scholar to identify studies published between January 2019 and January 2024. Studies on telemedicine adoption, barriers, and facilitators in India were included. Data were extracted and synthesized from 26 quantitative, qualitative, and mixed-methods research studies. Individual-level challenges include limited digital literacy, particularly among older adults and those in rural areas, and health literacy issues. Barriers such as limited smartphone access, unreliable internet connectivity, and socio-cultural preferences for in-person consultations were identified. Facilitators at this level include family involvement, training programs, and community outreach. Provider-level challenges involve a lack of training and concerns about care quality, while barriers include insufficient infrastructure and medico-legal concerns. Facilitators include ongoing training, clear guidelines, and user-friendly telehealth systems. System-level challenges include integrating telemedicine into existing infrastructure and ensuring data security. Barriers include inadequate funding and resistance to change, while facilitators include policy support, investment in infrastructure, and collaborative efforts. Telemedicine holds great potential to enhance remote healthcare access in India, but its successful implementation requires addressing significant challenges and barriers. Strengthening digital infrastructure, enhancing digital literacy, standardizing protocols, and developing clear regulatory frameworks are essential. Collaborative efforts and tailored approaches that respect local cultures can further facilitate telemedicine adoption. Continuous research and public awareness campaigns are necessary to ensure telemedicine's sustainable and effective use in India.

Implications of telemedicine

This article explores the transformative impact of telemedicine on spine surgery, tracing its evolution from historic roots to its expansion during the COVID-19 pandemic. The widespread adoption of telemedicine is discussed, highlighting some of its benefits such as increased patient accessibility and satisfaction. Despite its advantages, telemedicine introduces complex legal considerations, specifically concerning licensure, malpractice and data security. The article examines the challenges spine surgeons face when practicing telemedicine across state borders, the nuances of malpractice liability in virtual settings, and the importance of maintaining high standards of care. It also highlights the importance of adhering to HIPAA regulations, ensuring secure data transmission, and navigating reimbursement complexities. This article should serve as a thorough guide for spine surgeons navigating the evolving telemedicine landscape, balancing innovation with patient safety and legal accountability.

An Efficient Image Encryption Scheme for Medical Image Security

In the contemporary landscape of digital healthcare, the confidentiality and integrity of medical images have become paramount concerns, necessitating the development of robust security measures. This research endeavors to address these concerns by proposing an innovative image encryption scheme tailored specifically for enhancing medical image security. The proposed scheme integrates a sophisticated blend of symmetric and asymmetric encryption techniques, complemented by a novel key management system, to fortify the protection of medical image data against unauthorized access and malicious tampering. The proposed DNA-based encryption algorithm leverages the unique properties of DNA encoding to securely scramble image data, providing an added layer of protection. By utilizing DNA sequences in the encryption and decryption processes, the scheme achieves a high level of data confusion and diffusion, significantly enhancing security. The efficacy of the proposed encryption scheme is validated through comprehensive experimental evaluations, which demonstrate its proficiency in ensuring data security while maintaining computational efficiency. The scheme's compatibility with existing medical imaging systems is also examined, affirming its seamless integration into contemporary healthcare infrastructures. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. The primary contributions include the development of a DNA-based encryption algorithm and a novel key management system, both of which significantly enhance the security of medical images. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. By safeguarding the confidentiality and integrity of medical images, the proposed scheme empowers healthcare providers to uphold patient privacy and trust in the digital age. Experimental results show that this approach ensures robust encryption without compromising image quality, making it suitable for sensitive medical imaging applications.

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud

JointCloud computing represents a new generation cloud computing paradigm, which deeply integrates the cloud resources of multiple Cloud Service Providers (CSPs) to offer tailored cloud services to users. In contrast to traditional multi-cloud environment, JointCloud environment involve data circulation among multiple CSPs. However, in JointCloud environment, CSPs are not always fully trustworthy and they may illegally infringe upon users’ data privacy and security for their own benefit. Additionally, the heterogeneity arising from different data storage formats, structures, access control, and permission management mechanisms adopted by various CSPs makes achieving unified data management in JointCloud challenging. Therefore, to ensure secure storage and efficient circulation of data within JointCloud, it is essential to prevent violations for user privacy and data ownership, shield the heterogeneity of underlying data management mechanisms across different CSPs, and establish trusted transactions between CSPs. In this paper, we propose a framework called JointCloud Data Chain (JCDC) based on JointCloud computing and blockchain for data storage and circulation, aiming to ensure secure data storage and trustworthy transactions. JCDC utilizes blockchain to record data ownership and control data circulation, while integrating storage resources from various CSPs to construct a distributed off-chain Personal Data Storage (PDS) for expanding system storage capacity. Additionally, JCDC employs Certificateless Public Key Cryptography (CL-PKC) and Proxy Re-encryption technologies for user identity management and secure data transactions. Furthermore, smart contracts are designed to enable automated data storage and sharing. We conduct a security analysis of JCDC and develop a prototype system to validate its performance and practicality. Finally, extensive experimentation and analysis demonstrate that JCDC exhibits low time latency and cost, which makes it practical.

Meta-algorithmic optimized power allocation in cybertwin-based sixth generation cooperative communication system

The sixth-generation (6G) communication paradigm, powered by an Internet of Everything (IoE), promises unprecedented data density and transformative applications across various sectors like smart cities, healthcare, transportation, and agriculture. This study explores an emerging hybrid cybertwin-enabled 6G cooperative network architecture, focusing on secrecy capacity analysis. The approach involves Base Stations (BS) employing Non-Orthogonal Multiple Access (NOMA) techniques to transmit data to cybertwin hosts and servers via direct wireless links with Nakagami-m fading. Additionally, a Power Line Communication (PLC) link, subjected to Rayleigh fading, supplements the wireless link for communication between cybertwin hosts and servers. Bio-meta-heuristic optimization algorithms are introduced to optimize NOMA power allocation, specifically Grey Wolf Optimization (GWO) and a hybrid of Grey Wolf Optimization with Krill Herd Optimization (KHO). The system's capability to ensure secure data transmission while mitigating eavesdropping risks is assessed through thorough secrecy capacity and throughput analyses. Simulation results highlight the efficacy of the hybrid GWO-KHO-based NOMA power allocation scheme, showcasing superior efficiency over traditional methods. This study contributes novel insights into optimized power allocation and efficiency in cybertwin-enabled 6G networks, underscoring the significance of hybrid optimization approaches in future communication systems.

Concealing And Encrypting: Dual Approaches to Data Security in The Digital Age

Ensuring data security is a paramount concern in the contemporary digital landscape, given the rapid advancements in internet technology leading to a substantial surge in online transfer of both textual and multimedia information. The current modes of communication lack robust security measures, making data vulnerable to unauthorized access during transmission. Recognizing the critical importance of safeguarding electronic data, both public and private sectors employ various procedures and methods. Among the prominent technologies employed for data security are cryptography and steganography. Cryptography involves the art of encrypting information to hide its readable and significant contents, whereas steganography is a technique used to conceal data within a cover media, effectively masking the very existence of the data. It is imperative for organizations to adopt and integrate these security technologies to mitigate the risks associated with unauthorized data access and ensure the integrity of sensitive information.

Efficient three layer secured adaptive video steganography method using chaotic dynamic systems

In light of the unprecedented growth in internet usage, safeguarding data from unauthorized access has emerged as a paramount concern. Cryptography and steganography stand as pivotal methods for ensuring data security during transmission. This study introduces an innovative adaptive video steganography approach featuring three tiers of security for extracting concealed information, thereby facilitating secure communication. The embedding process operates within the spatial domain of cover video frames, enabling a remarkable hiding ratio of up to 28.125% (equivalent to 2.25 bits per pixel in payload) without compromising the quality of video frames. Users are afforded the flexibility to select between partial or full embedding capacity of CVF through the proposed adaptive control block (ACB). The chaotic key generator (CKG), which combines a logistic map and sine map, is employed to generate highly sensitive initial seeds for permutation order (PO), frame selection (FS), and random position for hiding (RPH), thereby ensuring three levels of security. Prior to transmission, both CVF and hidden data (SD) are encrypted using PO. Encrypted CVFs are then randomly selected using FS for embedding, with RPH employed during the embedding process. Subsequently, for transmitting the stego-video frame, embedded CVFs are decrypted using the same PO. Experimental results demonstrate the efficacy of the proposed approach, achieving an adaptive hiding ratio ranging from 7.1 to 28.125% (equivalent to 0.56 to 2.25 bits per pixel in payload) and maintaining a peak signal-to-noise ratio (PSNR) within the range of 50.25 to 62.05 dB.

Artificial intelligence in state and municipal management: challenges and prospects

Relevance. In the modern world, artificial intelligence (AI) is one of the fastest growing areas of information technology, which is increasingly penetrating into various spheres of human activity. In the context of public and municipal management, AI is a technology that is able to automate and optimize work processes, improve the quality of services provided to citizens, and increase the level of security and transparency of government work.The purpose is to analyze the possibilities and risks of using artificial intelligence in state and municipal management, as well as to determine the prospects for its development in this area.Objectives: to analyze the advantages of using artificial intelligence in state and municipal management; to study the risks and challenges associated with its implementation; to analyze trends in the development of artificial intelligence in this area, as well as to determine the prospects for its use.Methodology. Empirical, theoretical, statistical and graphical representation methods were used in the course of scientific research.Results. The study revealed the advantages of AI in public administration (improving efficiency, improving the quality of services, transparency, security); risks and challenges were studied (lack of infrastructure, risk of privacy violations, problems of transparency of algorithms, risk of discrimination); trends in the development of AI were analyzed (e-government, optimization of workflows, personalization of services, monitoring of social media, security); the prospects of using AI (smart cities, virtual assistants, hybrid systems, decision support, cooperation with business, international cooperation) have been identified.Conclusions. Artificial intelligence can significantly improve the efficiency and quality of public and municipal administration. However, for the successful implementation of AI, it is necessary to solve problems such as the lack of infrastructure and qualified specialists, ensuring data security, transparency of algorithms and compliance with the regulatory framework. To do this, it is necessary to conduct further research, develop integrated approaches and take into account world experience. The prospects for using AI include the creation of smart cities, the use of virtual assistants, hybrid systems, decision support systems, business cooperation and international cooperation.

A smart-contract-based adaptive security governance architecture for smart city service interoperations

Smart cities represent a promising paradigm aimed at enhancing citizens’ quality of life through cutting-edge infrastructure and technological advancements. Collaborative services serve as a cornerstone for any smart city, fostering seamless cooperation among diverse entities, including government agencies, businesses, and individuals, thereby enhancing community outcomes. These services are pivotal, promoting seamless communication and collaboration among various smart applications, and facilitating data exchange, resource sharing, and functional interactions within smart city environments to optimize efficiency, effectiveness, and user experiences. However, the development and deployment of secure, interoperable services in smart cities present significant challenges. These issues encompass, ensuring data security compliance during interoperation, effective management of interconnected services, securely handling sensitive data across services, and addressing issues related to confidentiality, integrity, and availability (CIA) traits. To tackle these challenges, this research proposes an innovative adaptive security governance framework tailored for smart cities. This framework relies on dynamic security policies implemented through smart contracts to guarantee data security and privacy during smart service interoperation. Real-world use cases in collaborative smart city environments validate the framework, integrating multi-chain blockchain technology, smart services APIs, and Software-Defined Networking (SDN), showcasing its ability to enhance security and efficiency in collaborative services. This study contributes to the development of safe and efficient collaborative services inside smart cities, tackling administrative issues while emphasizing data security and privacy. Smart cities may improve citizens’ living conditions while successfully addressing crucial security problems in an ever-changing environment by using this architecture.

An Approach to Secure Data Sharing in the Internet of Things Using Blockchain- Based Proxy Re-Encryption

The prevalence of data sharing has increased with the rise in popularity of cloud computing and the Internet of Things (IoT). However, ensuring data security is difficult because of the possible negative consequences of illegal data usage. This study presents a method for ensuring the secure transmission of data in cloud environments: proxy re-encryption. The identity-based encryption method enables data owners to transfer encrypted data to the cloud. Subsequently, a proxy re-encryption framework enables authorized individuals to access the data. To surpass the limitations of IoT device resources, we utilize an edge device as a proxy server to transfer computationally intensive tasks to a separate device. By utilizing the advantages of information-centric networking, we can optimize network capacity and enhance service quality by efficiently distributing cached content across the proxy. This method decreases bottlenecks in centralized systems while providing precise control over data access. The proposed work demonstrates the effectiveness of the strategy in ensuring data security, confidentiality, and integrity through thorough security research and plan review.

Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

With the evolution of next-generation communication networks, ensuring robust Core Network (CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service (DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture (BECNA) and the Secure Decentralized Identity Authentication Scheme (SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure (SPoF). The SDIDAS utilizes Decentralized Identity (DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

Advancing cryptographic security: a novel hybrid AES-RSA model with byte-level tokenization

As cyberattacks are getting more complex and sophisticated, stringent, multi-layered security measures are required. Existing approaches often rely on tokenization or encryption algorithms, both of which have drawbacks. Previous attempts to ensure data security have primarily focused on tokenization techniques or complex encryption algorithms. While these methods work well on their own, they have proven vulnerable to sophisticated cyberattacks. This research presents new ways to improve data security in digital storage and communication systems. We solve data security issues by proposing a multi-level encryption strategy that combines double encryption technology along with tokenization. The first step in the procedure is a byte-level byte-pair encoding (BPE) tokenizer, which tokenizes the input data and adds a layer of protection to make it unreadable. After tokenization, data is encrypted using Rivest–Shamir–Adleman (RSA) to create a strong initial level of security. To further enhance security, data encrypted with RSA has an additional layer of encryption applied using the advanced encryption standard (AES) method. This article describes how this approach is implemented in practice and shows how it is effective in protecting data at a higher level than single-layer encryption or tokenization systems.

The Effect of Smart Grid Technology on Energy Consumption Management in Canada

Purpose: The aim of the study was to evaluate the effect of smart grid technology on energy consumption management in Canada. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Smart grid technology has significantly enhanced energy consumption management in Canada. By integrating advanced metering infrastructure and real-time data analytics, smart grids optimize energy distribution, reduce peak demand, and enhance grid reliability. This technology enables better demand-response programs and promotes renewable energy integration, leading to more efficient energy use and reduced environmental impact. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & the theory of planned behavior (TPB) may be used to anchor future studies on the effect of smart grid technology on energy consumption management in Canada. Smart grid technology enables more efficient energy usage by providing consumers with real-time data and insights into their consumption patterns. The vast amounts of data generated by smart grids necessitate robust policies to protect consumer privacy and ensure data security. Policymakers should establish clear regulations governing the collection, storage, and use of smart grid data.

2FAKA-C/S: A Robust Two-Factor Authentication and Key Agreement Protocol for C/S Data Transmission in Federated Learning

As a hot technology trend, the federated learning (FL) cleverly combines data utilization and privacy protection by processing data locally on the client and only sharing model parameters with the server, embodying an efficient and secure collaborative learning model between clients and aggregated Servers. During the process of uploading parameters in FL models, there is susceptibility to unauthorized access threats, which can result in training data leakage. To ensure data security during transmission, the Authentication and Key Agreement (AKA) protocols are proposed to authenticate legitimate users and safeguard training data. However, existing AKA protocols for client–server (C/S) architecture show security deficiencies, such as lack of user anonymity and susceptibility to password guessing attacks. In this paper, we propose a robust 2FAKA-C/S protocol based on ECC and Hash-chain technology. Our security analysis shows that the proposed protocol ensures the session keys are semantically secure and can effectively resist various attacks. The performance analysis indicates that the proposed protocol achieves a total running time of 62.644 ms and requires only 800 bits of communication overhead, showing superior computational efficiency and lower communication costs compared to existing protocols. In conclusion, the proposed protocol securely protects the training parameters in a federated learning environment and provides a reliable guarantee for data transmission.

AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities

The project "AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities" addresses the pressing need for educational support in rural areas. With advancements in AI and natural language processing (NLP), chatbots have emerged as promising tools to bridge educational gaps. However, existing solutions often lack multilingual support and fail to cater to the unique needs of rural communities. This project aims to develop a multilingual chatbot tailored specifically for rural contexts, enabling access to educational resources and support in local languages. By leveraging AI and NLP technologies, the chatbot will provide personalized assistance to rural students, empowering them to pursue higher education aspirations. Through this project, we aim to address the challenges faced by rural communities in accessing quality education and contribute to their educational advancement. The methodology of the project involves leveraging machine learning algorithms and natural language processing techniques to develop the chatbot. Objectives include designing a user-friendly interface, implementing multilingual support, and ensuring robust data storage and retrieval. Design and experimental tools such as Python, Flask, NLTK, and scikit-learn will be utilized. Key specifications include handling diverse user queries, ensuring data security, and optimizing response generation algorithms. The sequence involves initial data gathering, followed by algorithm development, system testing, and iterative refinement based on user feedback. The key findings of the project showcase significant improvements in user interaction and system performance. Experimental data demonstrates high accuracy in query processing and response generation. The developed chatbot exhibits a working efficiency of over 90%, as indicated by user satisfaction surveys and performance metrics. These outcomes validate the effectiveness of the implemented methodologies and design choices. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)