iosr-JCE   Volume-27 ~ Issue-3 ~ May - Jun. 2025

Abstract: SUMMARY The research explored how assistive technologies have shaped the daily lives of people with hearing disabilities, focusing on everything from access to devices to their effects on social and educational inclusion. Combining qualitative and quantitative methods, the study brought together interview reports, online questionnaires, and case studies to build a faithful portrait of the user experience. The results revealed that, although these technologies significantly.......

Keywords: hearing disabilities; assistive technologies; social inclusion; accessibility; public policies

[1]. ALMEIDA, F. S. (2018). Assistive technologies and social inclusion: Challenges and perspectives for people with hearing disability. São Paulo: University of São Paulo Press.
[2]. BARBOSA, L. M., et al. (2021). Impacts of assistive technologies on the social inclusion of people with hearing disability. Journal of Social Inclusion, 9(3), 101-113.
[3]. COSTA, A. P., et al. (2021). Economic challenges in accessing assistive technologies in Brazil. Journal of Disability Studies, 5(1), 25-36.
[4]. GOMES, F. A., ALMEIDA, S. R. (2021). Public policies and the right to inclusion of people with hearing disability in Brazil. Brazilian Journal of Public Policies, 3(2), 45-58.
[5]. LIMA, Carolina Rodrigues. Training and use of assistive technologies: challenges for effective inclusion. Cadernos de Educação Especial, v. 31, n. 1, p. 155-170, 2019.

Abstract: This article addresses information security and data protection in the health sector, focusing on the elaboration of a policy aligned with ISO/IEC 27002 standards and the General Data Protection Law (LGPD). The research was conducted based on qualitative and quantitative methods, using data from sources such as the CERT.br and the MITRE CVE database. The results show the vulnerability of hospital systems to cyber attacks, with emphasis on phishing attempts, malware and ransomware, in addition to the identification of critical flaws in electronic medical record systems and IoT devices.........

Key Word: Information security; Data protection; Digital health; ISO/IEC 27002; LGPD; Hospital cybersecurity; Electronic medical records; Vulnerabilities (CVEs).

[1]. ANDERSON, Ross. Security Engineering: A Guide to Building Dependable Distributed Systems. 2. ed. Indianapolis: Wiley, 2001.
[2]. BRAZIL. General Law for the Protection of Personal Data (LGPD) – Law No. 13,709, of August 14, 2018. Available at: https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm. Accessed on: May 15, 2025.
[3]. DONEDA, Danilo. From privacy to the protection of personal data: elements of the formation of the General Data Protection Law. Journal of Contemporary Civil Law, São Paulo, n. 19, p. 13-41, Jan./Apr. 2019.
[4]. FONTES, R. A. Information Security: a practical approach. São Paulo: Novatec, 2015.
[5]. GOMES, C.; LAPÃO, L. V. Adoption of IT Security in Health Sector: The Portuguese Case. International Journal of Medical Informatics, v. 77, n. 5, p. 291–299, 2008.

Abstract: Background: Modern hiring has been transformed by the increasing use of online interviews and video resumes. However, assessing these multimedia applications remains time-consuming and subjective. This study aims to develop an AI-driven system to automate and standardize the evaluation process for video-based job applications. Materials and Methods: : The proposed system integrates Natural Language Processing (NLP), Automatic Speech Recognition (ASR), and Computer Vision (CV) to assess candidates’ communication skills, visual presentation, and resume relevance. It employs CNNs for.......

Key Word: video resume, AI in recruitment, NLP, ASR, computer vision, resume parser, job recommendation

[1]. Bhor, S.; Shinde, H.; Gupta, V.; Nair, V.; Kulkarni, M.; “Resume Parser Using Natural Language Processing Techniques”, Int. J. Res. Eng. Sci. 2021, 9 (6), 1–6.
[2]. Kulkarni, T.; Pardeshi, Y.; Shah, Y.; Sakat, V.; Bhirud, S.; “App for Resume-Based Job Matching with Speech Interviews and Grammar Analysis: A Review”, arXiv 2023, arXiv:2311.14729.
[3]. Karlsson, V.; “Concept of Interactive Video in Job Application”, Linnaeus University: V¨axj¨o, Sweden, 2019.
[4]. Khan, K.K.; “Project Management App for Aspen Surgical Products”, Grand Valley State University: Allendale, MI, USA, 2014.
[5]. Singh, R.; Yadav, H.; Sharma, M.; Gosain, S.; Shah, R.R. “Automatic Speech Recognition for Real-Time Systems”, IEEE International Conference on Multimedia Big Data (BigMM), Dubai, UAE, 9–11 December 2019; pp. 189–198. https://doi.org/10.1109/BigMM.2019.000-0..

Abstract: Lung diseases provide considerable difficulties for diagnosis and therapy, frequently necessitating accurate and prompt identification for efficient care. Deep learning models have become extremely effective tools in recent years for automating the diagnosis of lung diseases by analyzing data from medical imaging tests, especially CT and X-rays. To improve diagnosis accuracy and efficiency, this research focuses on creating a sophisticated deep learning framework for feature extraction and categorization of lung disorders. We are using different CNN models in this project: VGG16, VGG19, and DenseNet121. We aim to compare the models to determine which one performs......

Key Word: VGG16, VGG19, DenseNet121, Pneumonia Detection, Tuberculosis Detection Classification, Normal detection, Medical Image Analysis, Transfer Learning, Computer-Aided Diagnosis (CAD), Image Preprocessing, Disease Classification Model

[1]. Rajpurkar, P., Irvin, J., Zhu, K. et al. (2017). "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning". They used a DenseNet-121 CNN to detect pneumonia on chest X-rays with high accuracy.
[2]. Lakhani, P., & Sundaram, B. (2017). "Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks". Radiology. They used a CNN to classify TB on X-rays, achieving strong diagnostic performance.
[3]. Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019). z"An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare". Journal of Healthcare Engineering. They applied transfer learning with VGG19 and ResNet50 to classify pneumonia cases.[4].
Hwang, E. J. et al. (2019). "Development andvalidation of a deep learning–based automated detection algorithm for major thoracic diseases on chest radiographs". JAMA Network Open. This study involved a CNN to detect diseases like TB and pneumonia on radiographs.
[5]. Souza, J. C. et al. (2019). "An unsupervised method for pulmonary tuberculosis detection using chest X-ray images". Computers in Biology and Medicine. They combined feature extraction techniques and clustering to identify TB.

Abstract: Ad-hoc networking is the process by which users create a temporary network without the supervision of a central administrator. Each node in the network functions as both a host and a router, therefore it must be willing to send packets to other nodes. A routing protocol is necessary to accomplish this task. Ad hoc networks have unique properties that throw additional demands on the routing system. The most important property is the dynamic topology produced......

Key Word:Wireless Ad Hoc Networks, Mobility, AODV, DSDV, DSR, Protocols

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[2]. Ali, M. A. (2011). Security Issues regarding MANET ( Mobile Ad Hoc Networks ): Challenges and Solutions. March.
[3]. Almotairi, K. H., & Shen, X. S. (2015). A distributed multi-channel MAC protocol for Ad Hoc wireless networks. IEEE Transactions on Mobile Computing, 14(1), 1–13. https://doi.org/10.1109/TMC.2014.2316822
[4]. Azgin, A., Altunbasak, Y., & Alregib, G. (2005). Cooperative MAC and routing protocols for wireless ad hoc networks. GLOBECOM - IEEE Global Telecommunications Conference, 5, 2854–2859. https://doi.org/10.1109/GLOCOM.2005.1578280
[5]. Bhandari, R. (2024). Journal of Recent Innovations in Computer Science and Technology Load Balancing in Wireless Mobile Ad Hoc Networks Journal of Recent Innovations in Computer Science and Technology. 01(01), 8–14.

Abstract: Bird species classification plays a crucial role in ecological research, biodiversity monitoring, and wildlife conservation. In this study, we propose a machine learning-based image classification system for identifying bird species using a custom dataset of 20 bird species. We explore and compare the performance of a Convolutional Neural Network (CNN) and MobileNetV2 architecture for classification. With a total of 3408 images divided into training, validation, and test sets, we fine-tuned both models and found that MobileNetV2 significantly outperformed......

[1]. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR.
[2]. Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. CVPR.
[3]. TensorFlow Documentation. https://www.tensorflow.org
[4]. Brownlee, J. (2019). Deep Learning for Computer Vision: Image Classification Projects. Machine Learning Mastery.
[5]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR

Abstract: The retail industry, particularly small and medium- sized businesses (SMBs), faces significant challenges in leveraging transactional data to optimize inventory management, customer engagement, and sales forecasting. Existing tools often lack integration between analytical techniques, resulting in fragmented insights. This paper introduces a comprehensive retail analytics system that unifies Market Basket Analysis (MBA), customer segmentation, and machine learning– driven sales forecasting into a single platform. The system employs the Apriori and FP-Growth algorithms to identify product associations, RFM (Recency, Frequency, Monetary) analysis with K- Means clustering......

Keywords— Market Basket Analysis, Customer Segmentation, RFM-analysis methodology, Demand Forecasting

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[3]. C. I. Hermina, A. B. Aishwaryalakshmi, and B. Gopalakrishnan, “Market Basket Analysis for a Supermarket,” Int. J. Management, Technology and Engineering, vol. 12, no. 11, pp. 106–108, Nov.
2022. [Online]. Available: https://www.researchgate.net/publication/36548909 8
[4]. M. S. Kasem, M. Hamada, and I. Taj-Eddin, “Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing,” arXiv preprint arXiv:2302.01786, 2023.
[5]. A. G. Abad and L. I. Reyes-Castro, “Collaborative Filtering using Denoising Auto- Encoders for Market Basket Data,” Proc. 2017 Industrial and Systems Engineering Conf., Aug. 2017. [Online].Available: https://arxiv.org/abs/1708.04312

Abstract: This paper explores the changing role of digital banking in transforming financial accessibility for underserved communities across the United States. Against persistent economic inequities and structural barriers to traditional banking services, the rise of digital-only banks presents a potential inflection point in the national financial environment. Utilizing artificial intelligence (AI), biometric authentication, and mobile-first platforms, these institutions provide unparalleled convenience, affordable financial products, and instant access, benefits that are particularly important for low-income households......

Keywords:— Digital Banking, Financial Inclusion, Underserved Communities, Digital Literacy, Fintech Education, Regulatory Gaps, AI in Finance, Mobile Banking Platforms, Equitable Access.

[1]. Adeyelu, Oluwatobi & Ugochukwu, Chinonye & Shonibare, Mutiu. (2024). ETHICAL IMPLICATIONS OF AI IN FINANCIAL DECISION–MAKING: A REVIEW WITH REAL WORLD APPLICATIONS. International Journal of Applied Research in Social Sciences. 6. 608-630. 10.51594/ijarss.v6i4.1033.
[2]. Aldboush, H. H. H., & Ferdous, M. (2023). Building Trust in Fintech: An Analysis of Ethical and Privacy Considerations in the Intersection of Big Data, AI, and Customer Trust. International Journal of Financial Studies, 11(3), 90. https://doi.org/10.3390/ijfs11030090
[3]. AlHares, A., Zaerinajad, Z., & Al Bahr, M. (2024). Customer awareness and cyber security in the Organisation for Economic Co-operation and Development countries [Special issue]. Corporate & Business Strategy Review, 5(1), 371–381. https://doi.org/10.22495/cbsrv5i1siart11
[4]. Austin Telco Federal Credit Union. (2024). Understanding the Biggest Disadvantages of Payday Loans. Retrieved from https://www.atfcu.org/about/telco-blog/understanding-the-biggest-disadvantages-of-payday-loans#:~:text=High%20Interest%20Rates%20and%20Fees&text=The%20annual%20percentage%20rate%20(APR,is%20repaid%20in%20two%20weeks
[5]. Baraka, Kelly. (2024). Digital Divide and Social Inequality. International Journal of Humanity and Social Sciences. 3. 30-45. 10.47941/ijhss.2083.