Volume-12 , Issue-7 , Jul - 2024 Go Back

This research paper investigates the development of a Django-based e-commerce platform specializing in the sale of electronic goods, augmented with a user-based collaborative filtering algorithm for personalized product recommendations. In the competitive landscape of online retail, providing tailored recommendations to users is crucial for improving user engagement and driving sales. Leveraging Django framework, SQLite3 database, AJAX technology, and PayPal integration , this study explores the integration of collaborative filtering into the e-commerce framework to enhance user experience and boost sales. key features of this platform includes a search bar, brand and category filters, an administrative interface, shopping cart functionality, and integration with PayPal payment gateway. Subsequently, the research details the incorporation of a user-based collaborative filtering algorithm for product recommendations.

E-commerce, Django, SQLite3, Ajax, PayPal, Collaborative Filtering, Electronics.

[1] S. Gupta and A. Singh, "Personalized Product Recommendations in E-commerce Using Collaborative Filtering and Deep Learning," 2023 IEEE International Conference on Data Science and Machine Learning (ICDSML), New York, NY, USA, pp.112-116, 2023.
[2] J. Patel and R. Shah, "Dynamic User Preferences Modeling for Personalized Recommendations in E-commerce Using Temporal Collaborative Filtering," 2023 IEEE International Conference on Big Data and Analytics (ICBDA), Sydney, Australia, pp.45-49, 2023.
[3] M. Sharma and P. Mishra, "Privacy-Preserving Collaborative Filtering for Personalized Product Recommendations in Ecommerce," 2023 IEEE International Conference on Privacy, Security and Trust (PST), Toronto, Canada, pp.220-224, 2023.
[4] K. Mehta and S. Jain, "Real-time Personalized Product Recommendations in E-commerce Using Apache Spark and Collaborative Filtering," 2023 IEEE International Conference on Cloud Computing and Big Data (CCBD), Barcelona, Spain, pp.88-92, 2023.
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[9] N. Gupta and R. Sharma, "Context-Aware Personalized Recommendations in E-commerce Using Collaborative Filtering," 2023 IEEE International Conference on Internet of Things (IoT), Paris, France, pp.375-380, 2023.
[10] A. Verma and S. Kumar, "Adversarial Attacks and Defenses in Personalized Product Recommendation Systems," 2023 IEEE International Conference on Cybersecurity and Privacy (ICSP), Seoul, South Korea, pp.135-139, 2023.
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[12] Yash Patil, Samidha Ashtikar, Sakshi Shirodkar, Krishna Dudhate, Shraddha V. Pandit, "Recommendation Systems in Online Retail: A Comprehensive Survey of AI Techniques", International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.30-36, 2024.
[13] E. Gupta and R. Singh, "Multi-Modal Collaborative Filtering for Personalized Recommendations in E-commerce Using Graph Neural Networks," 2023 IEEE International Conference on Multimedia and Expo (ICME), Amsterdam, Netherlands, pp.25-29, 2023.
[14] B. Kumar and S. Sharma, "Federated Learning for Personalized Recommendations in Decentralized E-commerce Environments," 2023 IEEE International Conference on Parallel and Distributed Systems (ICPADS), Taipei, Taiwan, pp.190-194, 2023.
[15] R. Gupta and A. Kumar, "Hybrid Collaborative Filtering and ContentBased Filtering for Personalized Recommendations in E-commerce," 8 2023 IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD), Rome, Italy, pp.55-59, 2023.
[16] D. Sharma and S. Patel, "Sequential Collaborative Filtering for Personalized Recommendations in E-commerce Using Recurrent Neural Networks," 2023 IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, , pp.300-305, 2023.

Yash Patil, Samidha Ashtikar, Sakshi Shirodkar, Krishna Dudhate, Shraddha V. Pandit, "ProtonMart - AI Driven E-Commerce Platform For Electronic Goods Using Collaberative Filtering Algorithm," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.1-8, 2024.

India is a developing country. The 65% of India`s people live in villages, whose main occupation is agriculture. India has certainly made progress in the field of information technology. The IT advancement and technology is direct impact on agriculture. After the advent of the 21st century, modern agricultural technology got a boost in India. In the present era, farmers are moving towards farming using modern and scientific methods. AI based technology is the foundation of modern technology. Equipped with modern equipment and applications for prevention of pests and diseases in crops. The AI technology quickly and speedily identify the diseases occurring in crops can very easily treated with accuracy high accuracy. In this review, we have studied a lot of AI and their sub-domain machine learning (ML) method application in agriculture, especially on crop leaf diseases. ML technology can be used to identify leaf disease in the captured images.

Machine Learning, Tomato disease, CNN, Artificial Intelligence (AI), Agriculture, Disease, Food Crops

[1] N. Dawn et al., “Implementation of Artificial Intelligence, Machine Learning, and Internet of Things (IoT) in revolutionizing Agriculture: A review on recent trends and challenges,” International Journal of Experimental Research and Review, Vol.30, 2023, doi: 10.52756/ijerr. 2023.v30.018.
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[3] A. Siddiqua, M. A. Kabir, T. Ferdous, I. B. Ali, and L. A. Weston, “Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations,” Agronomy, Aug., Vol.12, No.8, 2022. doi: 10.3390/agronomy12081869.
[4] S. Bhuvaneswari, R. Surendiran, R. Aarthi, M. Thangamani, and R. B. Lingisetti, “Disease Detection in Plant Leaf using LNet Based on Deep Learning,” International Journal of Engineering Trends and Technology, Sep., Vol.70, No.9, pp.64–75, 2022, doi: 10.14445/22315381/IJETT-V70I9P207.
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[9] J. Andrew, J. Eunice, D. E. Popescu, M. K. Chowdary, and J. Hemanth, “Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications,” Agronomy, vol. 12, no. 10, Oct. 2022, doi: 10.3390/agronomy12102395.
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[11] A. M. Roy and J. Bhaduri, “A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision,” AI (Switzerland), Sep., Vol.2, No.3, pp.413–428, 2021. doi: 10.3390/ai2030026.
[12] M. Chohan*, A. Khan, R. Chohan, S. H. Katpar, and M. S. Mahar, “Plant Disease Detection using Deep Learning,” International Journal of Recent Technology and Engineering (IJRTE), May, Vol.9, No.1, pp.909–914, 2020. doi: 10.35940/ijrte. A2139.059120.
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[14] M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” Frontiers in Plant Science, Frontiers Media SA, Vol.14, 2023. doi: 10.3389/fpls.2023.1158933.
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[20] M. M. Hasan et al., “Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration,” Agriculture (Switzerland), Vol.13, No.8, Aug. 2023, doi: 10.3390/agriculture13081549.
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[22] M. Chohan*, A. Khan, R. Chohan, S. H. Katpar, and M. S. Mahar, “Plant Disease Detection using Deep Learning,” International Journal of Recent Technology and Engineering (IJRTE), vol. 9, no. 1, pp. 909–914, May 2020, doi: 10.35940/ijrte. A2139.059120.
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Gajendra Tandan, Asha Ambhaikar, "A Comprehensive Review of Crop Disease Identification Through Modern Artificial Intelligence Technology," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.9-15, 2024.

Skin cancer, one of the most common malignancies worldwide, necessitates early detection for better patient outcomes and efficient treatment. Recent advancements in deep learning have shown significant promise in enhancing the accuracy and efficiency of skin cancer diagnosis. This review comprehensively examines the current state of deep learning-based approaches for skin cancer detection, highlighting key methodologies, datasets, and performance metrics. We explore the integration of Convolutional Neural Networks (CNNs), Transfer Learning, and Attention Mechanisms in dermatological imaging analysis. Additionally, we discuss the impact of modified attention mechanisms, such as spatial and channel attention, in improving model performance by focusing on critical features of skin lesions. The review also addresses challenges related to data quality, class imbalance, and model interpretability. By synthesizing findings from recent studies, this review aims to provide a detailed understanding of how deep learning technologies are transforming skin cancer detection and to identify future research directions that could further enhance diagnostic accuracy and clinical applicability.

Deep Learning, Machine Learning, Convolutional Neural Network, Transfer Learning, Attention Mechanism, Skin Cancer Detection

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Su Myat Thwin, "Deep Learning Based Detection Approaches on Skin Cancer Detection: A Review," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.16-23, 2024.

Human Activity Recognition (HAR) plays a pivotal role in various domains, ranging from healthcare to surveillance and robotics. This paper offers a comprehensive detail of Convolutional Neural Network (CNN)-based methodologies in HAR, emphasizing their efficiency in accurately recognizing human activities from video data. We used the UCF50 dataset, which contains videos of 50 different human activities, making it a suitable benchmark for evaluating CNN-based HAR models. The study investigates the utilization of CNNs for feature extraction and classification in HAR, focusing on techniques such as frame extraction, data preprocessing, and model architectures. Detailed analysis of convolutional layers, pooling layers, and activation functions within CNNs showcases their ability to capture intricate spatial and temporal features. The research also delves into the benefits of data augmentation and normalization in enhancing model performance and generalization. The findings highlight the significant advantages of CNNs in capturing spatial information and improving accuracy in HAR tasks, making them highly effective for real-world applications across various domains.

Convolutional Neural Network, Human Activity Recognition, UCF50 Dataset, Data Preprocessing, Frame Extraction, Model Architecture, Feature Extraction

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[11] Souradeep Ghosh, "Proposal of a Real-time American Sign Language Detector using MediaPipe and Recurrent Neural Network", International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.46-52, 2021.
[12] Chalavadi Sravanth, Gadde Harshavardhan, Kamineni. Kavya, Shaik Mohammad Akbar, Ch.M.H. Sai Baba, "Real-Time Human Detection in Video Surveillance", International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.44-50, 2021.

Harshavardhan Patil, Priti Malkhede, Shreyash Madake, Ashutosh Kokate, Yash Bhandure, "Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.24-32, 2024.

The recent rapid growth of cloud service providers as more and more users and organizations are moving towards the multi-cloud systems, so that data can be accessed from any part of the world but it poses a humongous problem related to security and privacy of data. Cloud industry needs robust data security system. This research study investigates the feasibility, challenges, and potential impacts of implementing an automated data deletion system, integrated with the capabilities of intrusion detection, in a multi-cloud environment. Through qualitative methods, the aim to understand the experiences, perspectives, and insights of key stakeholders involved in the deployment and operation of such systems. Data collection methods include surveys of focused groups with cloud security experts, IT managers, compliance officers, and developers and an in-depth analysis of existing models and architectures, internal reports, whitepapers, policy documents, compliance guidelines, and security incident records. This research provides an insight and in-depth understanding of the requirements of the individual users and stakeholders of various organizations and improving the overall efficiency of multi-cloud environments by implementing the proposed Automated Data Deletion System with Intrusion Detection System.

Cloud Computing, Data Privacy, Data Security, Automated Data Deletion System, Intrusion Detection System

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Jashanbir Singh, Gurjit Singh Bhathal, "Securing Multi-Cloud Environment: An Automated Data Deletion System with Integrated Intrusion Detection System Over Multi-Cloud Platforms," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.33-40, 2024.

Smart farming is a significant application of the Internet of Things (IoT). It minimizes water, fertilizer, and crop yield waste. Sustainable development is confronted with two of the world`s most pressing problems: the food shortage and the rapid increase in population. Utilizing the infrastructure to use modern technologies like the Internet of Things, big data, and the cloud, smart farming has evolved into a systematic method of managing and monitoring sustainable agriculture. Data optimization is necessary for IoT-cloud systems due to the large amounts of data involved, both organized and unstructured. IoT-based smart farming improves the farming system by monitoring fields of crops. The IoTs in agriculture enable farmers to save time and reduce the consumption of resources such as water. The main aim of this review article is to offer a comprehensive analysis from a social and technological perspective. Important application domains, IoT architecture, and various problems are covered in this paper. In addition, the paper explores the available literature and shows their impact on many parts of the Internet of Things.

Smart farming, Internet of Things, modern technologies, big data, IoT-cloud systems, IoT architecture

[1] A. Dahane, R. Benameur, B. Kechar and A. Benyamina. An IoT Based Smart Farming System Using Machine Learning, International Symposium on Networks, Computers and Communications (ISNCC), Montreal, QC, Canada, pp.1-6, 2020. doi: 10.1109/ISNCC49221.2020.9297341.
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Garima Mathur, Vaibhav Tripathi, "Advanced Strategies for Enhancing Smart Farming Through Innovative IoT Techniques," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.41-47, 2024.

The rapid proliferation of fake news across digital platforms has emerged as a challenging task, undermining public discourse, and compromising public trust in media. Initially, the detection efforts focused on textual features using traditional machine learning algorithms, which, despite their effectiveness, were limited by the manual and time-consuming process of feature extraction. The advent of deep learning heralded a significant shift, with Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) offering enhanced capabilities in capturing the nuanced interplay of textual elements. Parallelly, the examination of visual features through multimodal methods demonstrated the importance of incorporating images and videos, further refined by Graph Convolutional Networks (GCNs) and attention mechanisms for superior accuracy. However, challenges persist in integrating and fully utilizing multimodal information, particularly in addressing the limitations of deep versus shallow feature analysis and the adaptability of models across diverse scenarios. This paper synthesizes the methodologies, findings, and critical evaluations of these approaches, highlighting the advancements and identifying areas for future research in the detection of fake news.

Fake News Detection, Textual Feature Extraction, Visual Feature Analysis, Multimodal Analysis, Machine Learning Algorithms, Deep Learning

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[6] Z. Shahbazi and Y. -C. Byun, "Fake Media Detection Based on Natural Language Processing and Blockchain Approaches," in IEEE Access, Vol.9, pp.128442-128453, 2021. doi: 10.1109/ACCESS.2021.3112607.
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[12] Zhang, Guobiao, Anastasia Giachanou, and Paolo Rosso. "SceneFND: Multimodal fake news detection by modelling scene context information." Journal of Information Science (2022): 01655515221087683.
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Avnis Kumar, Chetan Agrawal, Pooja Meena, "Advancements and Challenges in Fake News Detection using Machine Learning: A Comprehensive Review," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.48-52, 2024.

The integrity and security of federated learning systems, in which several users work together to train a single model while maintaining privacy, are seriously threatened by backdoor attacks. In this paper, we propose a preventive approach that includes differential testing and outlier detection mechanisms to identify and mitigate the risks associated with backdoor attacks. Federated learning aims for high model accuracy and performance, and the proposed preventive measures help maintain the integrity and reliability of the collaborative learning process. Differential testing is used to detect possible deviations or inconsistencies in the distribution of training data across multiple participants. By comparing the performance of models on different subsets of data, the presence of a backdoor attack can be identified. This differential testing framework acts as an early warning system, enabling the detection of introduced model biases or malicious attempts at data manipulation. Anomaly detection methods are also employed to find abnormalities or peculiar patterns that can point to the existence of a backdoor attack. When an outlier substantially deviates from the federated learning system`s expected behavior, it is identified and marked for additional research. This approach improves the robustness of federated learning models against malicious participants and manipulated data. Object-Oriented Analysis and Design (OOAD) techniques were used to ensure a structured and methodical design process. Python programming language was used for model implementation and simulation. The suggested defense strategy, which makes use of a federated CNN model, successfully reduces the possibility of backdoor attacks in federated learning systems. Other benchmark systems were compared with the suggested model. The results of the proposed system are stronger than existing systems as it achieves an accuracy of 99.95% in training and 99.97% in testing. In conclusion, we have detected backdoor attacks with different counts using both outlier and differential tests and prevented backdoor attacks (Differential, Gaussian_Backdoored, Gradient_Backdoored, Integral, Julia_Backdoored, Metaphor_Backdoored, Non-Backdoored, Pixel_Distort, Relu_Backdoored) using federated learning.

Framework, Prevention, Backdoor, Attacks, Learning and Outlier Detection)

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C. B. Biragbara, O.E. Taylor, D. Matthias, "A Framework for Prevention of Backdoor Attacks in Federated Learning Using Differential Testing and Outlier Detection," International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.53-59, 2024.