Dr. P. Anusha, R.V. Kannadasn, S. Faisal, K. Manojkumar, T. Santhakumar

Obtaining and managing a student’s details plays a vital role in academic database management and getting specific student details from the database becomes a very time-consuming process. Where the solution we bring to that problem is Face recognition-based Data Extraction. We will be implementing face recognition to detect the student’s face and match the detected face in the database to get that student’s details. To train the dataset we are using OpenCV (computer vision) a Python library. And we are training the dataset using classifiers. Once the face detection Is done and matched in the database it will display the output in a graphical user interface (GUI) we created using Tkinter.

Face Detection; Face Recognition; Dataset Preparation; Dataset Training; ; Haar Cascade; Classification Model; CNN.

[1] Chaitra T.K, M.C. Chandrashekhar, Dr. M.Z. Kurian (August 2018), Attendance Management System Using Face Recognition, Journal of Emerging Technologies and Innovative Research(JETIR), Volume 5 Issue 8 eISSN: 2349-5162,
[2] R. Raj Bharath, K. Arunkumar, S. Diwakar, P. Dineshkumar (July 2020), Partial Face Recognition Using Dynamic Feature Matching and CNN, Journal of Emerging Technologies and Innovative Research(JETIR), Volume 7 Issue 7 eISSN: 2349-5162,.
[3] Aanchal Singh, Jyoti Kansari, Vivek Kumar, Sinha (April-2022), Face Recognition Using Transfer Learning by deep VGG16 model, Journal of Emerging Technologies and Innovative Research(JETIR), Volume 9 Issue 4 eISSN: 2349-5162,.
[4] Dongshun Cui, Guanghao Zhang, Kai Hu, Wei Han, Guang-Bin Huang(Feb-2019), Face recognition using total loss function on face database with ID photos, ScienceDirect, Optics & Laser Technology Volume 110, Pages 227-233,.
[5] Ali Moeini, Karim Faez, Hossein Moeinin, Armon Matthew safai (Jan-2017), Open-set face recognition across look-alike faces in real-world scenarios, ScienceDirect, Image and Vision Computing Volume 57, Pages 1-14, Open-set face recognition across look-alike faces in real-world scenarios – ScienceDirect.
[6] Shuji Deng(2023), Face expression image detection and recognition based on big data technology, ScienceDirect, International Journal of Intelligent Networks Volume 4, Pages 218-223, Face expression image detection and recognition based on big data technology – ScienceDirect.
[7] Yahya Zennayi, Soukayna Benaissa, Hatim Derrouz, Zouhair Guennoun (Sep-2023), Unauthorized access detection system to the equipment in a room based on the person identification by face recognition, ScienceDirect, Engineering Applications of Artificial Intelligence Volume 124, 106637,
[8] Feng Zhao, Peng Zhang, Ran Zhang, Mengwei Li (Sep-2023), DGFaceNet: Lightweight and efficient face recognition, ScienceDirect, Engineering Applications of Artificial Intelligence Volume 124, 106513, https://doi.org/10.1016/j.engappai. 2023.106513.
[9] Divya Pandey, Priyanka Pitale, Kusum Sharma (Oct-2020), Face Recognition Based Attendance System using Python, Journal of Emerging Technologies and Innovative Research (JETIR), Volume 7, Issue 10, ISSN-2349-5162, JETIR2010064.pdf.
[10] Vivek Prakash Yadav, Shashi Kant Sharma (April-2019), FACE Recognition System: Review, Journal of Emerging Technologies and Innovative Research (JETIR), Volume 6, Issue 4, ISSN-2349-5162, face Recognition System (jetir.org).

G. Jagajothi, M. Jothiga

Those who are blind or have low vision have a far more difficult time travelling independently. There have been attempts to address the needs of the visually handicapped, but the problems that these solutions have failed to address have persisted. To help the visually impaired with everyday navigation and recognizing persons around them, the project proposes a novel concept for a smart blind stick equipped with a facial recognition technology. A smart blind stick, equipped with many sensors, will be developed as part of this project to assist the visually impaired. The specially-designed stick can detect potential dangers such as stairs, water, vibration, and fire, and it can then notify the visually impaired individual of these threats through haptic and auditory input. A blind person’s loved ones may be immediately notified of any assistance needs with the Stick’s “help me” button, and the device’s Internet of Things (IoT) modem allows for real-time position tracking. An on-board camera allows the blind person to capture images of obstacles as they arise, classify them using object recognition and deep learning, and then get audio cues to let them know what they’re up against. The proposed method establishes a connection to a server in the cloud by means of Internet of Things protocols and an Android web application.

Arduino UNO; Ultrasonic Sensor; Obstacles; Visually Impaired; Blind; Walking Stick; Internet of Things

[1] S Pruthvi, Pushyap Suraj Nihal, Ravin R Menon, S Samith Kumar and Shalini Tiwari, “Smart Blind Stick using Artificial Intelligence”, International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 5S, May 2019, ISSN 2249 – 8958.
[2] B. Kumar, R. Jagadeesh, C.S. Kumar, G. Srinivas and C. Gowri, “Accident Detection And Tracking System Using Gsm Gps And Arduino”, Journal of emerging technologies and innovative research, 2020. Mukesh Prasad Agrawal and Atma Ram Gupta, “Smart Stick for the Blind and Visually Impaired People”, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).
[3] Sneha Rao and Vishwa Mohan Singh, “Computer Vision and Iot Based Smart System for Visually Impaired People”, 2021 11th International Conference on Cloud Computing Data Science & Engineering (Confluence).
[4] Alaa H. Ahmed, Ihab A. Satam and Mokhaled N. A. Al-Hamadani, “Design and Implement A Smart Blind Stick”, Journal of Advanced Research in Dynamical and Control Systems, August 2019.
[5] S Bele, S Ghule, A. Gunjal and N. Anwat, “Design and implementation of smart blind stick”, 2nd International Conference on Communication & Information Processing (ICCIP), 2020, April.
[6] G Srinivas, G.M. Raju, D. Ramesh and S. Sivaram, “Smart Blind stick connected system using Arduino”, IJRAR-International Journal of Research and Analytical Reviews, vol. 6, no. 2, pp. 934-939, 2019.
[7] N Dey, A Paul, P. Ghosh, C. Mukherjee, R. De and S. Dey, “Ultrasonic sensor based smart blind stick”, 2018 international conference on current trends towards converging technologies (ICCTCT), pp. 1-4, 2018, March.
[8] V Kunta, C Tuniki and U. Sairam, “Multi-functional blind stick for visually impaired people”, 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 895-899, 2020, June.
[9] R Sitharthan, M Rajesh, K. Madurakavi, J. Raglend and R. Kumar, “Assessing nitrogen dioxide (NO2) impact on health pre-and post-COVID-19 pandemic using IoT in India”, International Journal of Pervasive Computing and Communications, 2020.
[10] N. Loganathan, K. Lakshmi, N. Chandrasekaran, S. R. Cibisakaravarthi, R. H. Priyanga and K. H. Varthini, “Smart stick for blind people”, Proc. 6th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), pp. 65-67, Mar. 2020.
[11] Natarajan, Y. M and A. Canessane, “IoT based Smart Stick with Automated Obstacle Detector for Blind People”, 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 536-540, 2022.
[12] M. P. Agrawal and A. R. Gupta, “Smart Stick for the Blind and Visually Impaired People”, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 542-545, 2018.
[13] N. Sahoo, H.-W. Lin and Y.-H. Chang, “Design and implementation of a walking stick aid for visually challenged people”, Sensors, vol. 19, no. 1, pp. 130, Jan. 2019.
[14] T. L. Narayani, M. Sivapalanirajan, B. Keerthika, M. Ananthi and M. Arunarani, “Design of smart cane with integrated camera module for visually impaired people”, Proc. Int. Conf. Artif. Intell. Smart Syst. (ICAIS), pp. 999-1004, Mar. 2021.

A.D. Oguizu, O.S. Nweze

Triphala; Acupuncture; Taichong.

Background: A 42-year-old male with a documented history of uncontrolled hypertension despite consistent Telmisartan 40mg (Telma 40mg) use for three years presented with additional complaints of breathlessness. This case report explores the management approach and outcomes of a 12-day integrated intervention combining Triphala decoction and acupuncture (Taichong point) for this patient. Methods: The patient participated in a 12-day trial receiving Triphala decoction twice daily and daily acupuncture sessions targeting the bilateral Taichong (Liver 3) points. Blood pressure, weight, and body mass index (BMI) were monitored throughout the intervention. Results: Following the intervention, the patient exhibited significant improvements in blood pressure control, with systolic blood pressure decreasing by 20% and diastolic blood pressure by 13%. Additionally, weight and BMI demonstrated a 5% reduction. Notably, the patient achieved medication independence, discontinuing Telma 40mg by the 8th day. Conclusion: This single case report presents a patient with uncontrolled hypertension who experienced substantial improvements in blood pressure control, weight management, and medication independence following a 12-day integrated intervention using Triphala and acupuncture. While these findings are preliminary and require further investigation with larger studies, this case contributes to the growing exploration of integrative approaches for managing hypertension.

[1] AnchalaR., Kannuri, N. K., Pant, H., Khan, H., Franco, O. H., Di Angelantonio, E., & Prabhakaran, D. (2014). Hypertension in India: a systematic review and meta-analysis of prevalence, awareness, and control of hypertension. Journal of Hypertension, 32(6), 1170–1177. https://doi.org/10.1097/HJH.0000000000000146
[2] Baliga, M. S., Meera, S., Mathai, B., Rai, M. P., Pawar, V., & Palatty, P. L. (2012). Scientific validation of the ethnomedicinal properties of the Ayurvedic drug Triphala: a review. Chinese Journal of Integrative Medicine, 18(12), 946–954. https://doi.org/10.1007/S11655-012-1299-X
[3] Gupta, R. (2016). Convergence in urban-rural prevalence of hypertension in India. Journal of Human Hypertension, 30(2), 79–82. https://doi.org/10.1038/jhh.2015.48
[4] Gupta, R., Gaur, K., Ahuja, S., & Anjana, R. M. (2024). Recent studies on hypertension prevalence and control in India 2023. Hypertension Research : Official Journal of the Japanese Society of Hypertension. https://doi.org/10.1038/s41440-024-01585-y
[5] Jantrapirom, S., Hirunsatitpron, P., Potikanond, S., Nimlamool, W., & Hanprasertpong, N. (2021). Pharmacological Benefits of Triphala: A Perspective for Allergic Rhinitis. Frontiers in Pharmacology, 12. https://doi.org/10.3389/FPHAR.2021.628198
[6] Lai, H. C., Lin, Y. W., & Hsieh, C. L. (2019). Acupuncture-Analgesia-Mediated Alleviation of Central Sensitization. Evidence-Based Complementary and Alternative Medicine : ECAM, 2019. https://doi.org/10.1155/2019/6173412
[7] Li, C., & Kelly, T. N. (2014). Hypertension in India. Journal of Hypertension, 32(6), 1189–1191. https://doi.org/10.1097/HJH.0000000000000158
[8] Lin, J.-G., Kotha, P., & Chen, Y.-H. (2022). Understandings of acupuncture application and mechanisms. American Journal of Translational Research, 14(3), 1469–1481. http://www.ncbi.nlm.nih.gov/pubmed/35422904
[9] Man, T. M., Wu, L., Zhang, J. Y., Dong, Y. T., Sun, Y. T., & Luo, L. (2023). Research trends of acupuncture therapy for hypertension over the past two decades: a bibliometric analysis. Cardiovascular Diagnosis and Therapy, 13(1), 67–82. https://doi.org/10.21037/CDT-22-480/COIF
[10] Peterson, C. T., Denniston, K., & Chopra, D. (2017). Therapeutic uses of triphala in ayurvedic medicine. Journal of Alternative and Complementary Medicine, 23(8), 607–614. https://doi.org/10.1089/acm.2017.0083
[11] Phimarn, W., Sungthong, B., & Itabe, H. (2021). Effects of Triphala on Lipid and Glucose Profiles and Anthropometric Parameters: A Systematic Review. Journal of Evidence-Based Integrative Medicine, 26. https://doi.org/10.1177/2515690X211011038
[12] Prabhakaran, D., Jeemon, P., Sharma, M., Roth, G. A., Johnson, C., Harikrishnan, S., Gupta, R., Pandian, J. D., Naik, N., Roy, A., Dhaliwal, R. S., Xavier, D., Kumar, R. K., Tandon, N., Mathur, P., Shukla, D. K., Mehrotra, R., Venugopal, K., Kumar, G. A., … Dandona, L. (2018). The changing patterns of cardiovascular diseases and their risk factors in the states of India: the Global Burden of Disease Study 1990–2016. The Lancet Global Health, 6(12), e1339–e1351. https://doi.org/10.1016/S2214-109X(18)30407-8
[13] Rana, S., Palatty, P. L., Benson, R., Kochikuzhyil, B. M., & Baliga, M. S. (2022). Evaluation of the anti-hyperlipidemic effects of Triphala in high fat diet fed rats: Studies with two combinations. Ayu, 43(3), 98–104. https://doi.org/10.4103/ayu.AYU_74_19
[14] Zhao, X. F., Hu, H. T., Li, J. S., Shang, H. C., Zheng, H. Z., Niu, J. F., Shi, X. M., & Wang, S. (2015). Is acupuncture effective for hypertension? A systematic review and meta-analysis. PLoS ONE, 10(7). https://doi.org/10.1371/JOURNAL.PONE.0127019.

Krishna Priya M

Augmented Reality; Virtual And Augmented Reality; Automotive Industry.

Augmented reality (AR) is a visual based interactive technology which is helping people from kids to employees with their respective learning and working areas. The paper explains on how augmented reality is helping people across borders with educational, medical, industrial, sales, product launching with automobile and other electronic deliverables. The augmented reality area is empowering the work force with better visual learning methods during training. One such area with automobile industry case study is done and the results are derived out of that. It shows the better executed data results in number, less manual work involved, easy documentation and practicing training sessions in real world scenario. The best results of implementing and using augmented reality in many application areas shows the better return on investment (ROI)

[1] https://www.industryweek.com/technology-and-iiot/article/22027338/five-ways-ar-apps-will-enhance-industrial-work-in-next-five-years
[2] https://www.leadinnovation.com/english-blog/augmented-reality-automotive-industry
[3] https://www.augrealitypedia.com/augmented-reality-healthcare-applications/
[4] https://www.researchgate.net/publication/325059726_Systematic_Review_of_Augmented_Reality_in_Healthcare_Preprint

S.J.Sujitha, Dr. Tamilselvi.P

Privacy Preservation; Blockchain Security; Increasing Efficiency.

The handling and accessibility of Electronic Medical Records (EMRs) have been completely transformed by the growing integration of cloud computing and Internet of Things (IoT) technologies in the healthcare industry. This research presents a new method for implementing a lightweight blockchain-based access control system to meet the critical security and efficiency concerns in the sharing of fog- assisted IoT cloud-based EMRs. Thanks to the integration of data from several medical treatment applications and Internet of Things devices, the outsourcing of encrypted electronic medical records (EMRs) has become a fundamental aspect of the modern healthcare scene. This strategy has unmatched benefits, such as increased accessibility, efficient inter-professional collaboration, and a significant decrease in computational operation expenses. The goal of this research project is to maximise the effectiveness of EMR sharing while simultaneously strengthening the security of sensitive medical data. The suggested framework makes use of blockchain technology to provide a simple and safe access control system for the Internet of Things with assistance from Fog. By doing this, the system delivers strong resistance to unwanted access, transparent access, and data integrity.

[1] U. C. Yadav and S. T. Ali, ”Ciphertext policy- hiding attributebased encryption,” in Proc. Int. Conf. Adv. Comput., Commun. Informat. (ICACCI), Aug. 2015, pp. 2067-2071, doi:10.1109/ICACCI. 2015.7275921.
[2] P. Sanchol, S. Fugkeaw, and H. Sato, ”A mobile cloud-based access control with efficiently outsourced decryption,” in Proc. 10th IEEE Int. Conf. Mobile Cloud Comput., Services, Eng. (MobileCloud), Aug. 2022, pp. 1-8, doi: 10.1109/MobileCloud55333.2022.00008.
[3] C. Hahn, J. Kim, H. Kwon, and J. Hur, ”Efficient IoT management with resilience to unauthorized access to cloud storage,” IEEE Trans. Cloud Comput., vol. 10, no. 2, pp. 1008-1020, Apr. 2022, doi: 10.1109/TCC.2020.2985046.
[4] Xi, P., Zhang, X., Wang, L., Liu, W., & Peng, S. (2022). A review of Blockchain-based secure sharing of healthcare data. Applied Sciences, 12(15), 7912.
[5] Huang, Q. (2020).. Computers & Security, 99, 102010.
[6] Sun, X., & Huang, Q. (2020). A blockchain- based scheme for privacy-preserving and secure sharing of medical data. Computers & Security, 99, 102010.
[7] Liu, J., Li, X., Ye, L., Zhang, H., Du, X., & Guizani, M. (2018, December).
[8] Tanr?verdi, M. (2020). A systematic review of privacy-preserving healthcare data sharing on blockchain. J Cybersecur Inf Manag, 5(2 SI 1), 31-
[9] Chen, Y., Ding, S., Xu, Z., Zheng, H., & Yang, S. (2019). Blockchain-based medical records secure storage and medical service framework.
[10] Chen, Y., Ding, S., Xu, Z., Zheng, H., & Yang, S. (2019). Blockchain-based medical records secure storage and medical service framework. Journal of medical systems, 43, 1-9.
[11] Sharma, P., Borah, M. D., & Namasudra, S. (2021). Improving security of medical big data by using Blockchain technology. Computers & Electrical Engineering, 96, 107529.
[12] Liu, X., Wang, Z., Jin, C., Li, F., & Li G. (2019). A blockchain-based medical data sharing and protection scheme
[13] Tian, H., He, J., & Ding, Y. (2019). Medical data management on blockchain with privacy. Journal of medical systems, 43, 1-6.
[14] Wang, B., & Li, Z. (2021). Healthchain: A Future Internet, 13(10), 247.
[15] Gao, F., Tao, X., & Liu, S. (2018). Blockchain- based data preservation system for medical data. Journal of medical systems, 42, 1-13.
[16] Xi, P., Zhang, X., Wang, L., Liu, W., & Peng, S. (2022). A review of Blockchain-based secure sharing of healthcare data. Applied Sciences, 12(15), 7912.
[17] A case study on blockchain technology in healthcare: MedRec, a, A., Aria, A., and Halamka,
[18] J.D. In: 2016 IEEE Open&BigData Conference
[19] FoxNewsHealth: Hospitals Throughout England Are Hit by a “Ransomware” Cyberattack. Associated Press, May 2017
[20] Glaser, A.: The global ransomware attack-
[21] Recode has affected US hospitals (2017). Global EU cyberattack: hackers target NSA hospitals, according to Recode.net/2017/6/27/15881666
[22] Gul, O.; Al-Qutayri, M.; Yeun, C.Y.: The structure of an electronic health record system at the national level. In: 2012’s Cloud Computing
[23] Hendrick, E., Schooley, B., Gao, C.: CloudHealth: Creating a Sturdy Cloud Platform for Applications in Healthcare. In: IEEE 2013’s 10th Conference on Consumer.

Dr .Srinivasa Rao Kadari

Cloud Computing; Security Issues; Unauthorized Access; DoS; Threats

Cloud computing hides the details of the system implementation from the end users and developers. Applications runs on the undefined physical systems. Similarly, data is stored at undetermined locations. The systems administration is outsourced to others and accessed by the user globally. For the organizations making their transition to cloud, cloud security is an essential factor while choosing a cloud provider. The attacks are getting stronger day by day and so the security needs to keep up with it. For this purpose it is essential to pick a cloud provider who offers the best security and is customized with the organization’s infrastructure. Cloud security has a lot of benefits cloud security has become a top priority for most industries operating in the cloud environment. Cloud service providers and adopters don’t consider it as a discrete practice; instead, they embrace and consider it as a primary aspect of overall security practice and data protection strategy. From small and large to enterprise-level organizations, it has become a go-to solution for everyone at the current time. Automation and modern cloud technologies have made cloud security much more advanced and allow it to deal with security issues effectively. However, maintaining in-house cloud security was challenging, which is why many organizations emerged in the market to offer optimum cloud security solutions to everyone. Currently, many top organizations

[1] https://www.geeksforgeeks.org/security-issues-in-cloud-computing/
[2] https://www.checkpoint.com/cyber-hub/cloud-security/what-is-cloud-security/top-cloud-security-issues-threats-and-concerns/
[3] Cloud Computing: Concepts, Technology, and Architecture by Thomas Erl
[4] Cloud Computing- A hands on approachby Arshdeep Bahga & Vijay Madisetti
[5] https://computingforgeeks.com/top-open-source-cloud-platforms-and-solutions/
[6] https://www.educba.com/cloud-computing-service-providers/
[7] https://www.ubuntupit.com/best-cloud-os-the-experts-recommendation/
[8] https://www.outsource2india.com/software/azure-application-development-services.asp
[9] https://www.clouddefense.ai/cloud-security-trends/

Renuka A, Rohit M, Smera C,Sandeep J, Sreeja C S

AI Chatbots; Job Attrition; Workplace Dynamics; Quantitative Analysis; Qualitative Insights; Employee Satisfaction; Continuous Monitoring,

This research carefully investigates the implications of AI chatbots on job attrition, and through the combination of quantitative and qualitative analysis, it tries to go through a complex landscape of modern workplaces. With surveys, interviews as well as rate of attrition analysis, comprehensive knowledge is built which reveals that there is a relationship between adoption of AI and job disillusionment. The study also majors in the use of case studies and sentiment analysis that explains more about their personal experiences. The methodology stresses continuous monitoring, inclusive employee consultation and targeted training programs for enhanced adaptability. Eth- ical considerations such as privacy protection are pivotal to this study. Thus, the findings make specific suggestions for action while also functioning as an organizational compass for using AI chatbots without risking attrition due to no jobs so organizations can embrace technological integration into workforce well-being in future times.

[1] L. Andrea, Guzman, Seth C. Lewis, “Artificial intelligence and commu- nication: A Human– Machine Communication research agenda,” 2019.
[2] Quynh N. Nguyen, Anna Sidorova, “capabilities and user experiences: a comparative study of user reviews for assistant and non-assistant mobile apps,” Twenty-third Americas Conference on Information Systems, Boston, 2017.
[3] N. Kaushal, · Rahul Pratap Singh Kaurav, Brijesh Sivathanu, “Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis,” under exclusive license to Springer Nature Switzerland AG 2021.
[4] Lea Katalina Kivinen, ”AI-driven chatbot as a support tool for develop- ers during the onboarding process,” 27 April 2023.
[5] Vijay Pereira, Elias Hadjielias, Michael Christofi, Demetris Vrontis, ”A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective,” 30 August 2021.
[6] Wieslawa Gryncewicz, Ryszard Zygala, Agnieszka Pilch, ”AI in HRM: case study analysis. Preliminary research,” Procedia Computer Science, 225 (2023) vol, 2351–2360.
[7] F. Ted Tschang, Esteve Almirall, ”Artificial intelligence as augmenting automation: Implications for employment,” Academy of Management Perspectives. 35, (4), 642-659, 2021.
[8] Amisha Bhargava, Marais Bester, Lucy Bolton, Employees’ Perceptions of the Implementation of Robotics, Artificial Intelligence, and Au- tomation (RAIA) on Job Satisfaction, Job Security, and Employability, Journal of Technology in Behavioral Science (2021) 6:106–113.
[9] Pawan Budhwar Ashish Malik, M. T. Thedushika De Silva & Praveena Thevisuthan, ”Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda,” The International Journal of Human Resource Management, 33:6, 1065-1097, 06 Mar 2022.
[10] Demetris Vrontis, Michael Christofi, Vijay Pereira, Shlomo Tarba, Anna Makrides, Eleni Trichina, ”Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review,” The International Journal of Human Resource Management, 12 Feb 2021.
[11] Prasanna Tambe, Peter Cappelli, and Valery Yakubovich, ”Artificial Intelligence in Human Resources Management: Challenges and a Path Forward,” California Management Review, Volume 61, Issue 4, 15-42, 2019. https://doi. org/ 10.1177/0008125619867910
[12] Chenzhuoer Li, Runjie Pan, Huiyu Xin and Zhiwen Deng,”Research on Artificial Intelligence Customer Service on Consumer Attitude and Its Impact during Online Shopping,” Journal of Physics: Conference Series, 2020.
[13] Eleni Adamopoulou, Lefteris Moussiades, ” Chatbots: History, technology, and applications,” Machine Learning with Applications, 2 (2020).
[14] Dena F. Mujtaba, Nihar R. Mahapatra, ”Ethical Considerations in AI- Based Recruitment,” IEEE International Symposium on Technology in Society (ISTAS) Proceedin

M.Nandhini, R.Harini

Diabetic Retinopathy; Image Classification; Convolutional Neural Network; Resnet 18

An innovative approach to diabetic retinopathy classification utilizing convolutional neural networks (CNNs) is presented in this research. Diabetic retinopathy, a severe complication of diabetes and a leading cause of blindness worldwide, necessitates accurate and timely diagnosis for effective treatment and management. A deep learning framework based on ResNet-18 architecture is developed to automatically classify retinal images into different stages of diabetic retinopathy. The proposed model demonstrates noteworthy accuracy and efficiency. Experimental results on a large dataset validate the effectiveness of the approach, offering potential applications in clinical practice for early detection and intervention of diabetic retinopathy. The findings of this research offer significant implications for enhancing healthcare outcomes in ophthalmology through the integration of artificial intelligence.

[1] .Kocur, I., Resnikoff, S.. Visual impairment and blindness in Europe and their prevention. Brit J Ophthalmol (2002).
[2] Sculpher, M., Buxton, M., Ferguson, B., Spiegelhalter, D., Kirby, A.. Screening for diabetic retinopathy: A relative cost-effectiveness analysis of alternative modalities and strategies. Health Econ (1992).
[3] M. Alban and T. Gilligan, “Automated detection of diabetic retinopathy using fluorescein angiography photographs,” Stanford Technical Report, (2016).
[4] V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” The Journal of the American Medical Association, vol. 316, issue (22, Dec. 2016).
[5] Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, A.. Computer-aided diagnosis of diabetic retinopathy: A review. Comput Biol Med (2013).
[6] Acharya, R., Chua, C.K., Ng, E., Yu, W., Chee, C.. Application of higher order spectra for the identification of diabetes retinopathy stages. J Med Syst (2008).
[7] Acharya, U., Lim, C., Ng, E., Chee, C., Tamura, T.. Computer-based detection of diabetes retinopathy stages using digital fundus images. P I Mech Eng H (2009).
[8] Fleming, A.D., Philip, S., Goatman, K.A., Prescott, G.J., Sharp, P.F., Olson, J.A.. The evidence for automated grading in diabetic retinopathy screening. Current Diabetes Reviews (2011).

Jwalajose, Dr. B. Suresh Kumar

Artificial Intelligence; Dream Recording; Neuroscience; Wearable Devices; Sleep Research.

This study explores the feasibility of utilizing artificial intelligence (AI) technology for the recording and analysis of dreams. Participants were equipped with wearable devices embedded with AI algorithms designed to detect and record dream-related brain activity during sleep [1]. Results indicate a promising potential for AI-based dream recording methods, offering valuable insights into the nature of dreams and their neural correlates. Future research directions and implications are discussed.

[1] D. Nahavandi, R. Alizadehsani, A. Khosravi, and U. R. Acharya, “Application of artificial intelligence in wearable devices: Opportunities and challenges,” Computer Methods and Programs in Biomedicine, vol. 213, no. 1, p. 106541, Jan. 2022, doi: https://doi.org/10.1016/j.cmpb.2021.106541.
[2] Y. Nir and G. Tononi, “Dreaming and the brain: from phenomenology to neurophysiology,” Trends in Cognitive Sciences, vol. 14, no. 2, pp. 88–100, Jan. 2010, doi: https://doi.org/10.1016/j.tics.2009.12.001.
[3] A. Kebaili, J. Lapuyade-Lahorgue, and S. Ruan, “Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review,” Journal of Imaging, vol. 9, no. 4, p. 81, Apr. 2023, doi: https://doi.org/10.3390/jimaging9040081.
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N. Prabha, M.Sharmila, M. Arulprabhu

IDPS; HIDS; NIDS; Behaviour Profile; Saas; Paas; Iaas

Abstract — Intrusion detection is a security management system for a computer or computer networks. An Intrusion Detection System (IDS) is a key to detect and prevent malicious activities. The network must be trained to detect intrusions. Intrusion Prevention System (IPS) is defense mechanisms to detect malicious packets within network traffic and stop intrusions, blocking the aberrant traffic automatically before it does any. Network Intrusion Detection System (NIDS) used as a tool that provides the intrusion detection functionality by sniffing the network traffic in real-time and it performs intrusion detection through network connections and outside the host machine is more resistant to attacks by malware. In a traditional network, attacker’s entry monitoring, Intrusion detects and alert to the administrative user for network malicious activity by deploying IDS on key network points on user site. Cloud network IDS has to be placed at server site and entirely administered and managed by the service provider. Proposed efficient IDS administered and monitored by the user and expert advice for wireless sensor network administrator.

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