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An Integrated Representation & Compression Scheme Based on Convolutional Autoencoders with 4D-DCT Perceptual Encoding for High Dynamic Range Light Fields 8

An Integrated Representation & Compression Scheme Based on C...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Khaidem, Sally Sharma, Mansi Indian Institute of Technology Madras Department of Electrical Engineering Chennai India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798331531539

The emerging and existing light field displays are highly capable of realistic presentation of 3D scenes on auto-stereoscopic glasses-free platforms. However, the large size of light field data presents a significant drawback for 3D displays and streaming purposes. When a light field has a high dynamic range (HDR), its size increases drastically. This paper proposes a novel compression algorithm specifically designed for high dynamic range light fields that achieves perceptually lossless compression. The algorithm leverages inter-view and intra-view correlations within the HDR light field by treating it as a four-dimensional volume. This compression technique is based on the 4D-DCT Uniform Colour Space (4DDCT-UCS) algorithm. Further encoding of 4DDCT-UCS processed images using High-Efficiency Video Coding (HEVC) eliminates intra-frame, inter-frame, and intrinsic redundancies in HDR light field data. Our comparison with state-of-the-art coders like JPEG-XL and existing HDR video coding algorithms demonstrates the superior compression performance of the proposed scheme for real-world light fields. © 2024 IEEE.

关键词： Encoding (symbols)

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Apple Leaf Disease Detection Using Transfer Learning

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Annals of Data science 2025年第1期12卷 213-222页

作者： Wani, Ozair Ahmad Zahoor, Umer Shah, Syed Zubair Ahmad Khan, Rijwan Department of Computer Science & Engineering Islamic University of Science and Technology Jammu & Kashmir Awantipora India Department of Computer Science and Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India

Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant health, reduced production severely impacts the country’s economy. Traditional disease identification methods, relying on human experts, are slow, time-consuming, and impractical for large farms. Our proposed model utilizes a combination of pre-trained Resnet18, Alexnet, GoogLeNet, and VGG16 networks to classify apple tree leaves into categories such as healthy, black rot, apple cedar rust, and apple scab based on images. Various image enhancement techniques were employed to enhance the model’s accuracy. Ultimately, our model achieved an accuracy of 97.25% on the validation dataset, demonstrating excellent performance across various metrics. This suggests its potential for efficient and accurate plant health monitoring in the agricultural sector. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Blockchain based System for Secure Property Registration

Blockchain based System for Secure Property Registration

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Mehta, Nishu Rani, Rinkle Kalra, Nidhi Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350333602

The procedure of registering a property involves paying stamp duty, and registering the sales deed for the property you have purchased. Property registration is done at the office of the sub-registrar who has jurisdiction over the area in which you purchased it. Land/property registration is a long process necessitating numerous paper checks, in-person meetings, and cash exchanges, which results in significant delays and is both time- and money-consuming. In India, currently government is practicing the paper-based land registration system which raises significant problems like high time intricacy, centralized authority, physical visit to property site for authentication, high cost of transactions, proneness to human error, frauds involving property documents, lack of communication, third-party participation, ineffectiveness, timing issues and issue with ownership. These issues are getting worse every day;hence the registry procedure system needs to be improved. In this study, to overcome such challenges we will develop a tamper-proof, fraud-resistant, automated Blockchain - based system to create, issue, securely store, share and verify the Land Registration Records. The proposed system will also provide a digital locker all the stakeholders for keeping land record certificates. All the actions taken by different entities involved in the land record registration will be time-stamped, verified by the different nodes, and stored as a block which has a unique hash value, so that neither the hackers/spammers nor the insiders can issue a counterfeit certificate on a later date or modify/tamper the original certificate. © 2023 IEEE.

关键词： Smart contract

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Informative task classification with concatenated embeddings using deep learning on crisisMMD

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International Journal of computers and Applications 2025年第2期47卷 123-140页

作者： Jain, Tarun Gopalani, Dinesh Kumar Meena, Yogesh Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging platforms like Twitter(now X) have proven to be an invaluable resource for citizens to share the real-time updates in their locality. Building upon this premise, we are proposing a new hybrid robust approach that integrates transfer learning with selective features from Liwc and manually engineered features to achieve state-of-the-art results on the CrisisMMD dataset on the informative task. In our innovative approach, we harnessed the power of Deep learning combined with traditional ML practices to improve the model, we utilized OpenAI embeddings and minute details from the tweets like the sentiment, text polarity, Retweet address, links included etc. We employed a dense-dropout-based concatenate model for further processing. We established baselines of each technique individually as well as commonly used techniques like Tf-Idf, BoW, Glove embeddings. Notably, our proposed model exhibits a significant accuracy improvement, achieving an accuracy of 87.93%, surpassing the highest accuracy of traditional methods of 82.93% obtained with Tf-Idf and ensemble voting. Through this work, we conclude that there is a scope of improvement in disaster-related NLP work with the introduction of transformer-based architecture and Pretrained GPT which include a massive corpus of text improving the semantic coherency which in turn lead to higher evaluation metrics. This work in future can be extended to incorporate different embeddings as well as using techniques like Multi-headed attention as well as RNNs like GST, GRU, etc. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Modeling and Prediction of COVID-19 Trajectory using Ensemble Learning 14

Modeling and Prediction of COVID-19 Trajectory using Ensembl...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Parmar, Shabnam Rani, Rinkle Kalra, Nidhi Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In this research work, For the event of a model that may predict and forecast death or recovery because of Covid-19 symptoms, different styles of requisite symptoms should be captured correctly, and enough data should be available to coach models of ensemble learning for accurate results. This virus may be passed from an infected person’s lips and nose to tiny liquid particles through the air by activities like coughing, sneezing, speaking, or inhalation. Ensemble Learning helps to improve machine learning results by mixing different models. it’s very useful for prediction and forecasting. In ensemble learning, different sort of algorithms is available consistent with the kind of problem and use cases. It’s part of AI and computers, primary concentrate on a way to use the info and algorithms to simulate in the same fashion as humans learn and focus on how its accuracy will be increased successively. During this research, various feature selection and extraction techniques on the Covid19 dataset are accustomed select or extracting top features, So the model is trained to employ a patient’s symptoms and other information to predict whether the patient will endure or die from their disease (Covid-19).In this research work, various ensemble algorithms are used such as Random Forest (RF), Gradient Boosting (GB), Adda Boosting (AB), and Bagging (BAG) are used. to try and do a comparative analysis on the accuracy used normal trained data set and K Fold trained data set. Ensemble Algorithms RF, GB, and AB feature selection strategies all produced highly promising results with an accuracy of 97% on Selection Techniques, and with Feature Extraction Techniques RF and Bagging also produced promising results with an accuracy of 96%. © Grenze Scientific Society, 2023.

关键词： Forecasting

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IsharaVerse: A Bidirectional, Context-Aware, Multilingual Sign Language System 3

IsharaVerse: A Bidirectional, Context-Aware, Multilingual Si...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Kashish Chelsi Langdi, Ramjas Trivedi, Amit Kumar Thapar Institute of Engineering and Technology Department of Computer Science and Engineeering Patiala India

ISBN: (纸本)9798331523893

IsharaVerse, a Context-Aware Multilingual Sign Language Generation and Translation System, bridges communication gaps between signers (Deaf, Hard of Hearing, Mute communities) and non-signers. It supports Indian Sign Language (ISL), converting between digits, alphabets, words, and sentences. Text/audio input is converted to sign gestures using Rule-Based Mapping, while Sign Language Translation leverages CNN-based Deep Learning. A multilingual module integrates Google Translator, and an Emotion Recognition feature uses a DistilRoBERTa model. Achieving 90% accuracy in sign-to-text and seamless multilingual support, IsharaVerse promotes inclusion in social, educational, and professional contexts. © 2025 IEEE.

关键词： Deep learning

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A Review on Smart Healthcare employing Quantum Internet of Things

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IEEE engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

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M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

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Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Analysis and Prediction of COVID-19 Disease using Machine Learning 14

Analysis and Prediction of COVID-19 Disease using Machine Le...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Parmar, Shabnam Rani, Rinkle Kalra, Nidhi Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In this research, the symptoms and other factors of a patient are utilized to train machine learning algorithms to predict whether the patient would die from or recover from (COVID19). It is probable that the coronavirus (COVID19) will create the highly infectious Coronavirus illness COVID19 (SARSCoV2). By coughing, sneezing, speaking, or inhaling, this virus can be transmitted from an infected person's lips and nose to small liquid particles. The size of these tiny atoms' inhalation droplets and aerosols vary, with bigger droplets being larger than smaller atoms. COVID19 is transferred by inhalation or by touching your eyes, nose, or mouth with your fingers after meeting a contaminated surface. When a big number of people are present, the COVID-19 virus can spread rapidly. We will need to examine the COVID19 dataset to see which models are the most accurate in estimating fatality rates for the virus's most vulnerable victims. Machine learning is used to compute and evaluate the performance of a variety of prediction models. We have used K-nearest neighbor (KNN), Support Vector Machine (SVM) classifier, Gaussian naive Bayesian (GNB), Decision tree (DT), and Logistic regression (LR) for the prediction of death and recovery of symptomatic patients. In this research, a variety of feature selection and extraction strategies were used, and prediction accuracy for feature selection methods for the KNN model and feature extraction methods for the GNB model both reached up to 96 percent. The k-nearest neighbor has performed and predicted high accuracy of 96% in both feature selection and extraction techniques. © Grenze Scientific Society, 2023.

关键词： Support vector machines

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Early Prediction of Alzheimer’s Disease Using Ensemble Learning Models

Early Prediction of Alzheimer’s Disease Using Ensemble Lear...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2022

作者： Singh, Divjot Mishra, Ashutosh Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9783031151743

The human brain is the most complex part as it controls numerous operations of the human body. For the past few decades, works have been going on to study the working of brain and how to make models that can replicate its working. Artificial intelligence is one such advanced field that trains on data we collect and predicts the output like a human brain. On the other hand, there are many diseases related to brain that have been discovered over time. These diseases progress with the passage of time, and, due to their slow rate of progression, it becomes difficult to detect them at early stage. Alzheimer’s disease (AD) is one of the neurodegenerative diseases which damages the brain permanently and becomes untreatable at the higher stages. Thus, it becomes very important to detect AD at an early stage to increase the life span of the person detected. The main predicament is to detect AD at an early stage and the selection of features responsible for it. The objective of this study is to predict AD at an early stage and to identify the features that facilitate its early prediction using ensemble learning. The proposed multiple ensemble method uses the selected features to achieve an accuracy of 82.40% and with higher ROC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Brain

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