iosr-JCE   Volume-27 ~ Issue-2 ~ Mar. - Apr. 2025

Abstract: The integration of 2D-to-3D transformation algorithms with Transformer networks and Augmented Reality (AR) has brought immense innovation into immersive retail experience. The article discusses methodologies relying on Transformers in order to recover 3D shoe models from 2D images and introduce them into AR worlds. With the presented system, consumers have the opportunity to virtually try different shoes without needing to physically go to the shop, enhancing comfort, convenience, and customization. Based on examining recent trends and comparing different strategies, this paper provides an in-depth overview of the current scenario, real-world applications, and prospects of future AR-based virtual shoe fitting.

Keywords: Transformer, 2D-to-3D, Augmented Reality, Shoe Try-On, Deep Learning.

[1] Z. Lei, Y. Chen, Y. Yang, M. Xia And Z. Qi, "Bootstrapping Automated Testing For Restful Web Services," In Ieee Transactions On Software Engineering, Vol. 49, No. 4, Pp. 1561-1579, 1 April 2023, Doi: 10.1109/Tse.2022.3182663.
[2] P. Bide, N. Sharma, D. Sheikh And A. Baranwal, "Augmented Reality Indoor Navigation With Smart Appliance Control," 2023 International Conference On Modeling, Simulation & Intelligent Computing (Mosicom), Dubai, United Arab Emirates, 2023, Pp. 356-361, Doi: 10.1109/Mosicom59118.2023.10458768.
[3] F. Zulfiqar, R. Raza, M. O. Khan, M. Arif, A. Alvi And T. Alam, "Augmented Reality And Its Applications In Education: A Systematic Survey," In Ieee Access, Vol. 11, Pp. 143250-143271, 2023, Doi: 10.1109/Access.2023.3331218.
[4] H. Jeong, K. -S. Choi, Y. Son And Y. Yang, "Ada: Augmented-Reality Development Adaptor For Supporting Ar Glass," 2023 14th International Conference On Information And Communication Technology Convergence (Ictc), Jeju Island, Korea, Republic Of, 2023, Pp. 1513-1516, Doi: 10.1109/Ictc58733.2023.10392424.
[5] H. Vardhan, A. Saxena, A. Dixit, S. Chaudhary And A. Sagar, "Ar Museum: A Virtual Museum Using Marker Less Augmented Reality System For Mobile Devices," 2022 3rd International Conference On Issues And Challenges In Intelligent Computing Techniques (Icict), Ghaziabad, India, 2022, Pp. 1-6, Doi: 10.1109/Icict55121.2022.10064611.

Abstract: In this dynamic era of rapid technological advancement, the integration of digital technologies in the field of healthcare has widely proven to be a commendable innovation. The project aims to contribute effectively in healthcare sector by providing aid to people by the means of internet. It aims to provide reliable recommendation to patients with diseases on the basis of their medical records, health condition, reviews by patients on medications they received and ratings. The project works on Natural Language Processing, followed by training of the model, analysing its performance and improving it. The project works on two principle components: Review Mining and Rating Analysis. Review Mining is based on Sentiment Analysis of Reviews to comprehend the reviews of the patients. Rating Analysis will help us to determine the effectiveness of certain medicines which in turn will provide us a comparative efficacy of the medicine to cure respective diseases.

Keywords- Machine Learning, Medicine Recommendation, Review Sentiment, Sentiment Analysis, Feature Extraction.

[1] Kavyasree S Anil A, Richa Jain; A Review On Data Mining Techniques In Healthcare Sector; International Conference Of Innovative Computing & Communication (ICICC) 2022.
[2] Mahima Mohapatra, Mamata Nayak, Saswati Mahapatra; A Machine Learning Based Drug Recommendation System For Healthcare; Graduate Research In Engineering And Technology (GRET), 2022.
[3] Harna Bodele, Manju Tagde, Samiksha Rangari, Yash Jadhao, Tushar Kumre; Medicine Recommendation System Using ML; International Journal For Research In Applied Science & Engineering Technology (IJRASET), 2024.
[4] Roopa D E, 2Munzeera Sajid C M,K B Gouthami, Pavana M S, Prince Kumar Singh; Recommendation Of Drug Based On Its Reviews Using Machine Learning; International Journal Of Research In Engineering And Science (IJRES), 2022.
[5] Varun A.Goyal , Dilip J. Parmar, Namaskar I. Joshi, Prof. Komal Champanerkar; Medicine Recommendation System; International Research Journal Of Engineering And Technology (IRJET), 2020.

Abstract: Cloud computing helps mobile devices to process tasks generated from the environment due to the mobile device's limited processing capability, storage memory, and battery life. Due to the distance between cloud infrastructures and mobile devices together with the problems of network fluctuations and bandwidth issues, cloud computing introduces delay, increased energy consumption, and response time of mobile device's requests. Mobile edge computing was introduced to bring cloud computing closer to mobile devices through computation offloading to minimize delay, energy consumption, and response time of mobile devices. This improved quality of service can only be achieved with an efficient resource allocation algorithm to appropriately schedule tasks in MEC during computation offloading..........

Keywords- Mobile Edge Computing, Computation Offloading, Genetic Algorithm, Inertia Weight, Particle Swarm Optimization

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[2] N.E. Nwogbaga, B.M. Emewu, I.N. Ogbaga, Critical Analysis Of Cloud Computing And Its Advantages Over Other Computing Techniques, J. Multidiscip. Eng. Sci. Technol. 3 (2016) 3955–3960.
[3] N.E. Nweke, F. Henry, Michael, J. S And Nwogbaga, Main Challenges Hampering Cloud Computing Adoption By Business Organizations, Res. J. Inf. Technol. 2 (2015) 1–11. Http://Www.Researchjournali.Com/Pdf/1225.Pdf.
[4] N.E. Nwogbaga, R. Latip, L.S. Affendey, A.R.A. Rahiman, Attribute Reduction Based Scheduling Algorithm With Enhanced Hybrid Genetic Algorithm And Particle Swarm Optimization For Optimal Device Selection, J. Cloud Comput. 11 (2022) 1–17.
Https://Doi.Org/10.1186/S13677-022-00288-4.
[5] N.E. Nwogbaga, R. Latip, L.S. Affendey, A.R.A. Rizaan, Investigation Into The Effect Of Data Reduction In Offloadable Task For Distributed Iot-Fog-Cloud Computing, J. Cloud Comput. Adv. Syst. Appl. 10 (2021) 1–12.
Https://Doi.Org/Https://Doi.Org/10.1186/S13677-021-00254-6.

Abstract: In the era of rapidly evolving AI-driven applications, the demand for dynamic and context-aware voice-activated systems has surged significantly. This paper presents the Audio Assistant using Retrieval-Augmented Generation (RAG), an intelligent system designed to enhance human-computer interactions through seamless, real-time communication. The system integrates Speech-to-Text (STT) for converting spoken input into text, a RAG-powered Large Language Model (LLM) for generating contextually accurate responses, and Text-to-Speech (TTS) for delivering natural-sounding audio outputs. By leveraging advanced technologies such as Pgvector for efficient vector storage and retrieval, LangChain for robust language processing pipelines, and Groq APIs for state-of-.......

Keywords—Retrieval-Augmented Generation, Speech-to-Text, Text-to-Speech, Large Language Model, Natural Language Processing, Real-Time Interaction, Context-Aware Communication, Intelligent Assistant, User Privacy, Secure Deployment, Multimodal Interface, Transcription Services, Adaptive Voice Technology

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Abstract: The recruitment process can be time-consuming, with the initial task of sorting through numerous CVs (Curriculum Vitae) being one of the most challenging aspects for recruiters. In modern recruitment, many organizations prefer electronic job applications over traditional paper resumes. The proposed system aims to streamline the process for both job seekers and recruiters by facilitating job application submissions and CV screening. Recruiters can publish job openings and share specific job requirements, while job seekers can apply by submitting their CVs for relevant positions. The submitted CVs are then compared against the job profile requirements using advanced......

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