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International Conference on computing for Sustainable Global Development (INDIACom)

作者： D Sai Samhitha K Ashwini Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

The exploration of Mars has consistently been of utmost significance in understanding planetary development, suitability for life, and the possibility of establishing civilizations. Precise classification of Martian terrain is essential for effective navigation, identification of resources, and prevention of potential dangers, to enhance the effectiveness of Mars missions. Traditional methods of terrain categorization frequently depend on manual examination and have constraints in terms of scalability and precision. This paper introduces an innovative method that utilizes deep learning algorithms to completely transform the process of classifying Martian terrain. This study employed the Unet and FCN models to categorize the Martian surface by utilizing a wide variety of images obtained from the AI4MARS dataset, from which a collection of 5K to 15K images are extracted. The results show the effective performance, the FCN model attained an Intersection over Union (IoU) score of 73%, while the Unet model acquired a score of 60%. The results emphasize the capacity of deep learning to revolutionize the classification of the Martian landscape, providing a more precise and adaptable answer in contrast with traditional approaches.

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Infected Plants Leaf Detection Using Deep Learning Cnn-Svm Classifier

SSRN

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SSRN 2024年

作者： Sundaram, Pratyush Oun Zaidi, Md. Damodharan, D. Department of Computer Science and Engineering Galgotias University Greater Noida India School of Computing Science and Engineering Galgotias University Pin code-203201 India

Majority of our diet directly comes from plants. so, plants play a major role in making food cheap, clean, nutritional, and healthy for us. Plants disease is indeed a threat to human civilization as it can cause harm to crops that can result in lowering food access and availability while on the other hand also raising food prices. The economic growth of a country is also affected by agriculture contributes up to 25% of Gross Domestic Product (GDP). The studies of plant disease mean studying the visual patterns (that can be seen by human eyes) of the plant. studying plant disease needs lots of time and expertise in different domains. so to overcome this problem image processing is used. Processing of the image has prepossessing of the plant leaf as segmentation, diseases specific data color, and image filtration. The classification of plant leaves can be achieved using various techniques like Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Neural Networks. Using various properties such as intensity, color, and dimensions one can define the classifications. In this research paper, we will discuss different methods for plant disease prediction using their leaf. We are also going to discuss different algorithms that are used for segmentation and feature extraction. This research paper focuses on finding an efficient and smart way to detect plant disease which will use machine learning techniques. © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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EyeHelp: Predicting an AOI based on Eye Gaze for Patient Assistance

EyeHelp: Predicting an AOI based on Eye Gaze for Patient Ass...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Akshay, S. Dhanush, S. Nath, Aswin G. Amudha, J. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka India Computational Engineering Ruhr University Bochum Bochum Germany Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Karnataka Bengaluru India

In this research, we delve into the predictive capabilities of deep learning models for classifying Areas of Interest (AOIs) based on gaze patterns in patient assistance systems. Utilizing eye-tracking technology to capture gaze data consisting of x and y coordinates along with duration, we aim to discern the specific regions of interest on the screen. With 9 distinct AOIs identified through a 9-point calibration process, we leveraged Python to preprocess the dataset. Applying FFNN, LSTM, and GRU, EyeHelp achieved promising accuracies: 80% for FFNN, 96% for LSTM, and 93% for GRU. Comprehensive observations in terms of accuracy, recall, and F1-score metrics across AOI classes are provided. EyeHelp employs an open source integrated webcam;hence the approach is open and adaptable to various healthcare environments. By integrating this technology, our system can be seamlessly deployed in clinics, hospitals, and even home care environments. The utilization of integrated webcams facilitates cost-effective and non-intrusive monitoring, offering valuable insights into patient behavior and attention patterns. © 2025 The Author(s).

关键词： Home health care

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High Quality Compression and Transmission of Remote Sensing Images Based on Semantic Communication

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2025年

作者： Jiang, Yan Xie, Kun Ouyang, Yudian Wen, Jigang Zhang, Guangxing Liang, Wei Feng, Quan Hunan University College of Computer Science and Electronics Engineering Changsha China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Chinese Academy of Sciences Institute of Computing Technology China Hunan Vanguard Group Corporation Limited Changsha China

Remote sensing imagery plays a crucial role in areas such as environmental monitoring and urban planning. However, due to fragile communication links, limited bandwidth and harsh wireless environments, transmitting data from remote locations to ground applications faces the dilemma of high biterror rates, which have a poor impact on downstream missions. Semantic communication is a feasible solution that transmits only the semantic features of the raw data extracted using neural networks. Although effective, existing semantic communication methods cannot cope with high compression rate requirements and complex communication environments. Therefore, in this paper, an effective image compression and transmission framework ASE-JSCC is proposed. To minimize the transmitted data, we design a semantic extraction module and an important feature selection module to efficiently extract, select, and compress critical semantic features required for downstream tasks. To improve the communication robustness of the model in complex environments affected by variable channels, we optimize the source-channel joint coding technique by randomly adding noise with different types and sizes. Finally, we deploy ASE-JSCC to the scene classification task of remote sensing images and conduct extensive experiments on four real datasets, achieving classification accuracy of 84.29% - 88.62% under 384 times compression ratio, verifying the excellent performance of the proposed framework. © 2016 IEEE.

关键词： Image compression

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Assessing Susceptibility Factors of Confirmation Bias in News Feed Reading 25

Assessing Susceptibility Factors of Confirmation Bias in New...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Boonprakong, Nattapat Pareek, Saumya Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne Parkville VIC Australia School of Computing and Information Systems University of Melbourne Melbourne VIC Australia School of Computer Science and Engineering University of New South Wales Sydney NSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

Individuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases. © 2025 Copyright held by the owner/author(s).

关键词： cognitive bias confirmation bias individual difference recall social media

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A Deep Learning-based Smart System for Ornaments and Devices Detection during Check-In of Entrance Exams

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Procedia computer science 2024年 233卷 464-473页

作者： P S Krishnapriya Gundam Nikitha Kalla Vishwas Uday Kiran Kuruba Aparna Singamsetty Chandu Priya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Nowadays, individuals are experiencing an era characterized by a pandemic situation. Given the challenging circumstances face these days, it is imperative to develop a real-time model that automatically detects whether candidates are wearing ornaments or carrying any devices before appearing for an entrance examination, as it serves as a vital precautionary measure for our health and safety. The proposed system will function in two stages: firstly, it will employ the YOLOv7 algorithm, which operates on Convolutional Neural Networks (CNN), to detect any gadgets or ornaments worn by the candidate. Additionally, we incorporated Mobile Net and HairNet into the code, utilizing TensorFlow's Keras API. Notably, all the datasets used in this system have been created from scratch. Upon successfully detecting gadgets such as wristwatches, Bluetooth devices, earrings, and other materials, the system will promptly display a warning message, denying entry to the respective individuals. Moreover, the proposed system includes real-time monitoring of the examination hall through CCTV cameras. As a cost-effective, time-saving, and accurate solution, it can be implemented across various examinations to ensure candidates' and invigilators' safety and adhere to examination norms.

关键词： Convolutional Neural Networks (CNN) Real-time object detection Deep learning

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Sentiment Analysis Model using Text and Emoticons for Pharmaceutical & Healthcare Industries 2

Sentiment Analysis Model using Text and Emoticons for Pharma...

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Bhaskaran, S. Veeramanickam, M.R.M. Hariharan, S. Bharathiraja, N. Pradeepa, K. Marappan, Raja Sastra Deemed University School of Computing Thanjavur India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India St. Michael College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781665474160

Background: Nowadays, pharmaceutical and healthcare industries apply sentiment analysis (SA) to analyze customers' feedback on pharmaceutical products. SA uses natural language processing (NLP) to recognize, assess and analyze the pharmaceutical data to extract valuable insights about the sentiment it conveys. Over the past two decades, in addition to text, emoticons have become a conventional way of expressing one's sentiments. The researchers utilizing textual data for SA have largely ignored emoticons due to their complexity and insufficient resources. Aim of the study: Necessary to include emoticons in SA to capture the emotion expressed by pharmaceutical data. However, this research develops an algorithm that performs SA on text and emoticons. Methods: To highlight the significance of emoticons, the analysis has been conducted on text-only and text-and-emoticon data, using deep learning (DL) and machine learning (ML) methods on multiple datasets. The features - term frequency-inverse document frequency (TF-IDF), emoticon lexicons, and a bag of words (BoW) are taken for simulation. Results: Simulation of text features using BiLSTM provides better accuracy, between 85% and 90%, compared to other methods. The precision lies between 70% and 90%. The recall is lowered to 64%. The sentiment the emoticon conveys outweighs that of the text associated with it. In addition, the DL algorithms outperform the ML methods. The overall results indicate that considering emoticons along with text positively impacts ***, it is noted that the DL model marginally outperforms all of the ML algorithms used. Conclusions: This research analyzed the augmentations to the implementation, including multiple enhancements during the pre-processing stage, the inclusion of emojis, and the usage of a bi-long short-term memory network (BiLSTM) in the DL model. The experimental results conclude that better measures are obtained using the DL method. © 2022 IEEE.

关键词： Sentiment analysis

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Enhanced Diabetic Retinopathy Grading Using a Deep Hybrid Approach

Enhanced Diabetic Retinopathy Grading Using a Deep Hybrid Ap...

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International Conference on computing and Networking Technology (ICCNT)

作者： Gali Laharesh Gvs Sarath Chandra R Sakthi Vikkraman Sbs Mani Kumar M Suchithra Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In recent times there have been significant achievements in deep learning methodologies, the task of classifying retinal diseases is still challenging because of their minute pathological regions. The retina’s blood vessels can be compromised by diabetic retinopathy, a complication arising from diabetes falls under the distinct category of retinal diseases. Despite many attempts with classification models such as CNN and other deep learning approaches detection encounters several challenges. These include existing deep learning models are susceptible to biases towards either local or global perspectives. These biases can impact semantic-level understanding of these representations. This work proposes a hybrid framework for Diabetic retinopathy grading. Inspired from the achievements of vision transformers in image classification and its ability to capture spatial relationships in fundus images related to Diabetic retinopathy features. Augmenting this vision transformer with traditional multiple instance learning forming a hybrid model ViT-MIL. By combining ViT’s global learning capabilities with MIL’s effective local feature extraction this hybrid model achieves enhanced performance.

关键词： Deep learning Diabetic retinopathy computer vision Pathology Attention mechanisms Predictive models Transformers Feature extraction Retina Diseases

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Enhancing Disaster Awareness using BERT-Based Analysis of Twitter Chat

Enhancing Disaster Awareness using BERT-Based Analysis of Tw...

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International Conference on computing and Networking Technology (ICCNT)

作者： M Charan Kumar D. Spoorthi Keya G Bharathi Mohan M Rithani Gundala Pallavi R Prasanna Kumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Disaster management and response are critical for minimizing the impact of natural and man-made disasters. Social media platforms, particularly Twitter, have emerged as valuable sources of real-time information during disasters. This paper presents a novel approach to enhance disaster awareness through the analysis of Twitter chat using Bidirectional Encoder Representations from Transformers (BERT). BERT is a powerful natural language processing model that has shown remarkable performance in various text analysis tasks. This work illustrates BERT for analyzing tweets related to disasters to improve situational awareness. This work uses disaster-related tweets dataset and preprocess them for analysis and and the data is fine-tuned using pre-trained BERT model. This research demonstrate that the BERT-based approach outperforms traditional methods like BILSTM-Glove with accuracy of 84.25% and BERT-BILSTM-CNN with accuracy of 95% in classifying disaster-related tweets.

关键词： Measurement Accuracy Social networking (online) Disasters Blogs Bidirectional control Natural language processing Encoding Complexity theory Context modeling

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Accent-Enhanced Automatic Speech Recognition for Multilingual Classrooms

Accent-Enhanced Automatic Speech Recognition for Multilingua...

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International Conference on computing and Networking Technology (ICCNT)

作者： M Jagadeeshvaran H Sarah Michelle S Shruti Bala M Srinivasa Vinayak T Deepika Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In an increasingly interconnected world, universities face the challenge of hosting diverse students from different backgrounds. The biggest obstacle to effective communication and learning in multicultural classrooms is teacher and student diversity. To overcome this challenge, we propose the development of noise-enhanced automatic speech recognition (ASR), specifically for multilingual classrooms. The system is designed to provide instant feedback on lessons while preserving the subtleties of speech, thus improving understanding and creating a learning environment. Using the advanced capabilities of the Transformer model together with the convolutional neural network (CNN) highlight classifier, the proposed system provides powerful and accurate transcription capabilities. This article provides an overview of the design, implementation, and evaluation of voice-enhanced ASR, demonstrating its potential to transform communication and learning in a variety of educational settings.

关键词： Accuracy Transducers Face recognition Neural networks Transforms Speech enhancement Transformers Complexity theory Convolutional neural networks Automatic speech recognition

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