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

Abstract: Rapid and effective response systems are necessary for emergencies including pandemics, natural catastrophes, and cyber-attacks. In these kinds of scenarios, artificial intelligence (AI) has become a game-changing tool that helps with real-time decision-making and maximizes resource allocation. With an emphasis on machine learning algorithms, neural networks, and optimization techniques, this study investigates how AI might be incorporated into emergency response systems to enhance cyber security defenses, disaster management, and healthcare responses. The study......

[1] Chen, Y., Li, X., & Zhang, J. (2021). AI-Driven Disaster Prediction: A Review Of Machine Learning Models And Applications. Journal Of Disaster Research, 16(4), 453-468.
[2] Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis Using Machine Learning And Deep Learning Models. Medrxiv.
[3] Nguyen, T., & Shmatikov, V. (2020). Anomaly Detection With Machine Learning In Cybersecurity: Challenges And Opportunities. ACM Computing Surveys, 53(1), 1-30.
[4] Jiang, F., Ma, X., & Zhao, Z. (2019). AI-Driven Optimization Algorithms For Resource Allocation In Emergency Scenarios. International Journal Of Emergency Management, 13(2), 98-113.
[5] Mnih, V., Kavukcuoglu, K., Silver, D., Et Al. (2015). Human-Level Control Through Deep Reinforcement Learning. Nature, 518(7540), 529-533

Abstract: This thesis explores the classification and conversion of 2D medical images (X-ray, MRI, and CT) into 3D images, leveraging advanced AI and deep learning techniques. As medical imaging technologies continue to evolve, the need for efficient and accurate interpretation of medical data has become more significant. This work addresses the challenges in transforming 2D images, typically captured during routine diagnostic procedures, into 3D models, which can provide a more comprehensive understanding of the patient's condition. The proposed methods employ deep learning algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and other AI-based frameworks to perform this conversion while maintaining the integrity of the diagnostic data.

Keywords- Classification and Conversion of 2-Dimensional Image to 3-Dimensional Image (X-ray, MRI, CT images) Using AI and Deep Learning Techniques

[1] Shin, H. C., Et Al. (2016). "Deep Convolutional Neural Networks For Computer-Aided Detection: CNN Architectures, Dataset Characteristics And Transfer Learning." Journal Of Medical Imaging, 3(4), 1-15. Https://Doi.Org/10.1117/1.JMI.3.4.040503
[2] Liu, Y., Et Al. (2020). "3D Medical Image Reconstruction Using Deep Learning Techniques." Journal Of Healthcare Engineering, 2020, Article ID 8852642. Https://Doi.Org/10.1155/2020/8852642
[3] Litjens, G., Et Al. (2017). "A Survey On Deep Learning In Medical Image Analysis." Medical Image Analysis, 42, 60-88. Https://Doi.Org/10.1016/J.Media.2017.07.005
[4] Choi, W., Et Al. (2018). "3D Image Reconstruction From Multiple 2D Images For Medical Applications Using Generative Adversarial Networks (Gans)." IEEE Transactions On Medical Imaging, 37(3), 517-528. Https://Doi.Org/10.1109/TMI.2017.2779059
[5] Zhou, S. K., Et Al. (2018). "A Review On 3D Medical Image Reconstruction And Analysis Using Deep Learning." Computerized Medical Imaging And Graphics, 65, 11-23. Https://Doi.Org/10.1016/J.Compmedimag.2018.03.003.

Abstract: This study investigates the effectiveness of various deep learning and transfer learning models in classifying diabetic retinopathy (DR) using datasets including APTO, Messidor, and RFMiD. A comprehensive analysis is conducted using a range of state-of-the-art CNN architectures, including ResNet50, ResNet152, ResNet101, CNN, AlexNet, InceptionV3, InceptionResNetV2, Deep Residual Network, DenseNet201, DenseNet121, and Xception. While the base paper primarily focused on conventional CNN architectures, we extend this exploration by incorporating.........

Keywords- Diabetic Retinopathy, Deep Learning, Transfer Learning, CNN, Xception, Inception, ResNet, AlexNet, Deep Residual Network

[1] Pouya Saeedi, Inga Petersohn, Paraskevi Salpea, Belma Malanda, Suvi Karuranga, Nigel Unwin, Stephen Colagiuri, Leonor Guariguata, Ayesha A Motala, Katherine Ogurtsova, Et Al., "Global And Regional Diabetes Prevalence Estimates For 2019 And Projections For 2030 And 2045: Results From The International Diabetes Federation Diabetes Atlas," Diabetes Research And Clinical Practice, Vol. 157, Pp. 107843, 2019.
[2] Early Treatment Diabetic Retinopathy Study Research Group Et Al., "Grading Diabetic Retinopathy From Stereoscopic Color Fundus Photographs—An Extension Of The Modified Airlie House Classification: Etdrs Report Number 10," Ophthalmology, Vol. 98, No. 5, Pp. 786–806, 1991.
[3] Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Et Al., "Development And Validation Of A Deep Learning Algorithm For Detection Of Diabetic Retinopathy In Retinal Fundus Photographs," Jama, Vol. 316, No. 22, Pp. 2402–2410, 2016.
[4] Maryam Badar, Muhammad Haris, And Anam Fatima, "Application Of Deep Learning For Retinal Image Analysis: A Review," Computer Science Review, Vol. 35, Pp. 100203, 2020.

Abstract: Groceries shopping forms the everyday need of most the person. However, there is a large variety of almost similar products that can be found lined up at the shelf in the supermarket. Customers spend the most time in the supermarket while reading the ingredient list on the nutrition facts label to select the best product they want. Despite being time-consuming, the greatest challenge that a shopper faces is being able to limit and search for products that do not contain........

Keywords- Autism, Convolutional neural network (CNN); Artificial Neural Network (ANN); K- Nearest Neighbors (KNN); Logistic Regression (LR); Support Vector Machine (SVM) [5].

[1] Wei Zhu , Charles B. Owen , Hairong Li , Joo-Hyun Lee Personalized In-Store E-Commerce With The Promopad: An Augmented Reality Shopping Assistant
[2] PHARA: An Augmented Reality Grocery Store Assistant Conference: The 20th International Conference, September 2018 DOI:10.1145/3236112.3236161
[3] JUNHO AHN, JAMES WILLIAMSON, MIKE GARTRELL, RICHARD HAN, QIN LV, And SHIVAKANT MISHRA, Supporting Healthy Grocery Shopping Via Mobile Augmented Reality University Of Colorado Boulder. ACM Trans. Multimedia Comput. Commun. Appl., Vol. 12, No. 1s, Article 16, Publication Date: October 2015
[4] A Mobile Adviser Of Healthy Eating By Reading Ingredient Labels November 2016 Lecture Notes Of The Institute For Computer Sciences DOI:10.1007/978-3-319-58877-3_4 Conference: 6th EAI International Conference On Wireless Mobile Communication And Healthcare At: Milan, Italy.
[5] Juhwan Lee, Sangwon Hwang, Jisun Lee And Seungwoo Kang Article: Comparative Performance Characterization Of Mobile AR Frameworks In The Context Of AR-Based Grocery Shopping Applications Published: 24 February 2020