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Framework for Domain Independent Hybrid Recommender Systems: Addressing Challenges of Imperfect Data

Framework for Domain Independent Hybrid Recommender Systems:...

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vibhute, Amey Netaji Subhas Institute of Technology Department of Computer Engineering Delhi India

ISBN: (纸本)9798350382693

Recommender systems are an indispensable aspectof numerous contemporary online platforms, aiding users inuncovering new and pertinent content. However, both content-basedfiltering (CBF) and collaborative filtering (CF) systemsexhibit distinct strengths and limitations. CBF systems typicallyexcel at addressing cold start issues but their recommendationsmay falter when metadata is incomplete or absent. CF systems,on the other hand, can provide superior recommendationquality when ample data is accessible, but they may fail wheninsufficient data is available. To mitigate these limitations, thispaper puts forward a hybrid recommender system that harnessesthe strengths of both CBF and CF methodologies. Our systemassigns confidence scores to both models and dynamically assignsweights based on those scores to generate the most advantageousrecommendations. This approach empowers us to circumventthe drawbacks of each model and capitalize on their respectivestrengths. Our framework has been evaluated on a vast dataset ofuser ratings and movie metadata. The findings demonstrate thatour hybrid system outshines both CBF and CF systems in termsof recommendation quality, particularly for cold start users andunpopular items. Notably, our hybrid system empowers users tofine-tune the balance between the two models using a handfulof parameters which allows users to tailor the recommendationengine to their specific data and preferences. © 2024 IEEE.

关键词： Matrix factorization

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Landmark-Aware and Part-Based Ensemble Transfer Learning Network for Static Facial Expression Recognition from Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 349-361页

作者： Wadhawan, Rohan Gandhi, Tapan K. Netaji Subhas University of Technology Computer Science and Engineering Department Netaji Subhas Institute of Technology New Delhi110078 India Indian Institute of Technology Delhi Iit Campus Electrical Engineering Department New Delhi110016 India

Facial expression recognition from images is a challenging problem in computer vision applications. Convolutional neural network (CNN), the state-of-the-art method for various computer vision tasks, has had limited success in predicting expressions from faces having extreme poses, illumination, and occlusion conditions. To mitigate this issue, CNNs are often accompanied by techniques like transfer, multitask, or ensemble learning that provide high accuracy at the cost of increased computational complexity. In this article, the authors propose a part-based ensemble transfer learning network that models how humans recognize facial expressions by correlating visual patterns emanating from facial muscles' motor movements with a specific expression. The proposed network performs transfer learning from facial landmark localization to facial expression recognition. It consists of five subnetworks, and each subnetwork performs transfer learning from one of the five subsets of facial landmarks: eyebrows, eyes, nose, mouth, or jaw to expression classification. The network's performance is evaluated using the Cohn-Kanade (CK+), Japanese female facial expression (JAFFE), and static facial expressions in the wild datasets, and it outperforms the benchmark for CK+ and JAFFE datasets by 0.51% and 5.34%, respectively. Additionally, the proposed ensemble network consists of only 1.65 M model parameters, ensuring computational efficiency during training and real-time deployment. Gradient-weighted class activation mapping visualizations of the network reveal the complementary nature of its subnetworks, a key design parameter of an effective ensemble network. Lastly, cross-dataset evaluation results show that the the proposed ensemble has a high generalization capacity, making it suitable for real-world usage. © 2020 IEEE.

关键词： Face recognition

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Blockchain-based rumor detection approach for COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 435-449页

作者： Rani, Poonam Jain, Vibha Shokeen, Jyoti Balyan, Arnav Department of Computer Engineering Netaji Subhas University of Technology Delhi India Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak India

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts’ veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： COVID-19

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Breast Cancer Detection using Deep Learning 2

Breast Cancer Detection using Deep Learning

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Afaq, Shajal Singh, Nanhay Netaji Subhas Institute of Technology Department of Computer Science and Engineering New Delhi110031 India

ISBN: (纸本)9798350368109

Breast cancer is a leading cause of morbidity for women all over the world, BC, or breast cancer, affects many women globally. It ranks fourth in fatality among colon, brain, and cervical malignancies. New BC cases are predicted to rise 70% in the following two decades. Thus, early identification and accurate breast cancer diagnosis improve prognosis and long-term survival. Machine learning methods like "Support Vector Machines"(SVM) and Nearest Neighbor have shown promising but limited efficacy in recognizing BC. This study introduced a "Convolutional Neural Network"(CNN) model of utilizing deep neural networks to diagnose BC using the Wisconsin BC database. The research demonstrates the application of DL technology for diagnosing BC using the Kaggle Dataset. The research method involves data acquisition, data preprocessing and using the dataset to train the CNN model and assess its performance using four key metrics: precision, recall, accuracy, and F-measure. CNN model accuracy is 95.57%, precision is 97.70%, recall is 98.64%, and F-measure is 98.14%. This approach outperforms standard models, proving its effectiveness in detecting BC. © 2024 IEEE.

关键词： Lung cancer

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A secure framework to preserve privacy of biometric templates on cloud using deep learning

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Recent Advances in computer Science and Communications 2021年第5期14卷 1412-1421页

作者： Arora, Shefali Bhatia, M.P.S. Division of Computer Engineering Netaji Subhas Institute of Technology Delhi India

Introduction: Cloud computing involves the use of maximum remote services through a network using minimum resources via internet. There are various issues associated with cloud com-puting, such as privacy, security and reliability. Due to rapidly increasing information on the cloud, it is important to ensure security of user information. Biometric template security over cloud is one such concern. Leakage of unprotected biometric data can serve as a major risk for the privacy of in-dividuals and security of real-world applications. Methods: In this paper, we improvise a secure framework named DeepCrypt that can be applied to protect biometric templates during the authentication of biometric templates. We use deep Convolu-tional Neural Networks to extract features from these modalities. The resulting features are hashed using a secure combination of Blowcrypt (Bcrypt) and SHA-256 algorithm, which salts the templates by default before storing it on the server. Results: Experiments conducted on the CASIA-Iris-M1-S1, CMU-PIE and FVC-2006 datasets achieve around 99% Genuine accept rates, proving that this technique helps to achieve better performance along with high template security. Conclusion: The proposed method is robust and provides cancellable biometric templates, high security and better matching performance as compared to traditional techniques used to protect the biometric template. © 2021 Bentham Science Publishers.

关键词： Biometrics

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Monitoring maritime traffic with ship detection via Yolov4 2

Monitoring maritime traffic with ship detection via Yolov4

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2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022

作者： Verma, Gaurav Gupta, Aditya Bansal, Shobhit Dhiman, Himanshu Computer Engineering Netaji Subhas Institute of Technology Delhi India Electronics and Communications Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (纸本)9781665442909

In India a large part of goods transportation is carried out by sea, leading to an emerging requirement for remote maritime patrolling system, which also serves as an asset during wartime and peacetime for defence. In this research paper, we propose an automated maritime patrolling solution by making a Deep Learning Model Pipeline for Ship Detection from satellite images using existing State of the art Object Detection Algorithms like Faster-RCNN, SSD, YOLOv3, and YOLOv4. We compare results based on various Evaluation Metrics. Further we also release our own dataset which consists of around 300 satellite images of the top 13 busiest Sea-ports of India. After performing the validations, we found that the YOLO v4 displayed the best results with a balanced mAP and FPS score to detect the ships in the satellite images. © 2022 IEEE.

关键词： Object detection

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Implementing Blockchain technology in Healthcare: An Overview, Key Requirements, and Challenges 12th

Implementing Blockchain Technology in Healthcare: An Overvie...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Raj, Aman Verma, Reshu Kataoka, Kotaro Department of Computer Science and Engineering Patna India Netaji Subhas University of Technology New Delhi India Indian Institute of Technology Hyderabad India

ISBN: (纸本)9789819731794

This research aims to develop a theoretical design for a healthcare app that utilizes blockchain technology to manage patient and physician databases. The study identifies gaps in earlier models that primarily focused on the use of blockchain in the finance and economic sectors. The ultimate goal is to create a smartphone app that will enable surgeons to manage their patients more efficiently and help patients compare costs, procedures, and pre/post-surgery preparations using Bitcoin as payment. The research involves an analysis of the present literature on blockchain technology in healthcare and space missions and uses a conceptual framework to develop the theoretical design. The results of the study include the development of a theoretical design for the healthcare app that could increase efficiency in patient management, cost savings, and simplify the payment process. The study hopes to encourage medical stakeholders to participate in the blockchain network, which could lead to increased network sustainability and security. Overall, the research highlights the potential benefits of blockchain technology in healthcare, particularly in managing patient and physician databases, and proposes a design that could lead to increased efficiency and cost savings. The study aims to contribute to the existing conceptual framework for medical stakeholders and encourage their participation in the blockchain network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Intelligent personalised learning system based on emotions in e-learning

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Personal and Ubiquitous Computing 2023年第6期27卷 2211-2223页

作者： Karthika, R. Jesi, V. Elizabeth Christo, Mary Subaja Deborah, L. Jegatha Sivaraman, Audithan Kumar, Sanjeev Department of Computer Science and Engineering University College of Engineering Tindivanam Tamil Nadu Melpakkam604001 India Department of Networking and Communications School of Computing College of Engineering SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Information Technology Netaji Subhas University of Technology New Delhi110078 India Department of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi110078 India

One of the greatest challenges in the success of a personalised e-learning system lies in the behaviour identification of the learners during the learning and evaluation phase. The content delivery in an e-learning system must be modified and updated periodically according to the preference and behaviour of the learners. Usually, the behaviour of the learners drastically changes according to their affective states during the learning phase. The accurate identification of the learner’s negative emotions and addressing such emotions carefully in a positive sense can greatly provide success to the learners. In this paper, the learner’s emotions especially frustration emotion are automatically and accurately estimated using the information received from learning management systems (LMS) using the Takagi sugeno fuzzy inference engine. Based on this estimation, several motivational messages are distributed according to the identified emotions which had greatly helped them to succeed during e-learning. The motivational messages that are used for sending to the learners are based on regulatory fit theory. Several kinds of statistical tests are used in this paper for deep analysis. Two sets of learners, namely, control and experimental sets, are identified from undergraduate students. Experimental results are shown for these two sets to reveal the increase in their emotional strength after receiving the motivational messages. Statistical analysis t-test is also applied to the two sets, and the experimental results have shown that there is a significant difference between the two groups which shows the dominance of the proposed system. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： E-learning

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Quantum Field Current Modelling in a Semiconductor 3rd

Quantum Field Current Modelling in a Semiconductor

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Vaish, Arti Parthasarathy, Harish Department of Computer Science and Engineering School of Engineering and Technology O P Jindal University CG Raigarh Punjipathra India Electronics and Communication Division Netaji Subhas University of Technology Dwarka New Delhi India

ISBN: (纸本)9789819719426

In this paper, we model the sea of electrons and holes within a semiconductor using the second quantized electron-positron Dirac wave operator field interacting with an applied classical electromagnetic field, a quantum photon field which can be regarded as quantum fluctuations of the electromagnetic field, and a noisy quantum photon field coming from the bath outside the semiconductor. Essentially, in this model, the Dirac wave field interacts and evolves along with the quantum photon field in the given background classical electromagnetic field and the given quantum noisy operator electromagnetic field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photons

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Decision-based framework to facilitate EDGE computing in smart health care

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Multimedia Tools and Applications 2024年第38期83卷 85849-85869页

作者： Singh, Simranjit Sajwan, Mohit Kukreja, Sonal Department of Information Technology Dr B R Ambedkar National Institute of Technology Jalandhar Punjab India Department of Information Technology Netaji Subhas University of Technology New Delhi India School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida India

In the past few years, with the increase in population and health concerns, there has been a need for efficient health monitoring solutions that can help patients monitor their health consistently to be aware of any health risks at the initial stage. The advancement in sensing and smart technologies helps monitor human behaviors to predict health risks. In this work, a dynamic decision-based activity prediction system is proposed using Random Forest, SVM, Decision Trees, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) on an edge device. We train the models using features from the MHealth dataset, such as acceleration, rate of turn, and magnetic field, to predict activities such as standing, climbing, running, and jogging, collected from various sensors. Our framework dynamically selects between machine learning (ML) and deep learning (DL) algorithms based on real-time data size and edge device capabilities, ensuring optimal performance and resource utilization. The results for the proposed models are compared and analyzed. The experimental results indicate that among all machine learning methods, Random Forest achieves the highest overall accuracy at 98%, while in deep learning algorithms, both LSTM and GRU reach a maximum accuracy of 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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