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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Alfatmi, Khalid Pande, Sakshi Shahade, Makarand Suryawanshi, Vaibhavi Badgujar, Kalpesh Patil, Vishwajit Kulkarni, Mayuri Department of Computer Engineering SVKM’s Institute of Technology Dhule India

ISBN: (纸本)9781032642987

Educational institutions must keep pace with evolving technology, particularly in managing student placements. The current manual processes for students and Training and Placement Officers (TPOs) involve resume creation, job matching, and administrative tasks, emphasizing the need for automation and efficiency. PlaceTech: An AI-Enabled Solution for Smart Placement introduces a unified platform for students and TPOs to streamline placement processes. It offers a dynamic resume builder, AI-driven chatbot, and online assessments for students, while providing TPOs with efficient tools to manage job description postings and events. This innovation promises to enhance placement through smart automation and improved communication. Institutional and departmental TPOs gain the ability to categorize students, manage job descriptions, and analyze placement data, fostering greater efficiency. By centralizing and automating these operations, PlaceTech embodies a technology-driven approach, providing a scalable solution for streamlined placement process, with implications extending beyond education to various industries. © 2024 The Author(s).

关键词： Students

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Fast MIS on Incremental Graphs 31

Fast MIS on Incremental Graphs

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Trivedi, Aditya Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

Maintaining the Maximal Independent Set (MIS) in dynamic graphs is crucial for applications like resource allocation, load balancing, and network optimization. Traditional approaches require complete re-computation, making them inefficient for large and evolving graphs. Our proposed methods utilize Tuple Valued Bitmap (TVB) and its extension with levels, TVBL, to enhance parallelism and minimize search space. Experimental evaluations on large-scale graphs, ranging from 50 million to 1.2 billion edges, demonstrate significant speedups-up to 15.64x for insertions, with minimal impact on the MIS cardinality. This approach offers a scalable solution for dynamic graphs, paving the way for further optimization in fully dynamic and streaming settings. © 2024 IEEE

关键词： Resource allocation

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Addressing Class Imbalance for Improved Pneumonia Diagnosis: Comparative Analysis of GANs and Weighted Loss Function in Classification of the Chest X-Ray Images 5

Addressing Class Imbalance for Improved Pneumonia Diagnosis:...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Kulkarni, Radhika V. Paldewar, Ameya Paliwal, Sanket Ajinath, Palwe Amol Panchal, Soham Department of Computer Engineering Vishwakarma Institute of Technology Maharashtra Pune India

ISBN: (纸本)9798350361155

Pneumonia detection using the chest X-ray images is genuinely, a critical task in the medical field of study and research, still there are multiple prevailing challenges, that include the overfitting and the lack of generalization in the existing models, because of scarcity in data and class imbalance. In response, the study on the problem statement introduces a comparative analysis that aims at significant enhancement of accuracy in pneumonia diagnosis. Our approach highlights on evaluating the effectiveness of two distinct methodologies: a weighted loss function and Generative Adversarial Networks (GANs). By putting the class imbalance within medical datasets into consideration, the weighted loss function targets the underrepresented pneumonia cases. By prioritizing these classes during model training, the weighted loss function seeks to give a boost and attention on sensitivity and consider false positives and negatives effectively, thus refining the diagnosis process. Along with this, our study holds the potential of GANs for data augmentation, aiming to almost nullify the impact of data scarcity. By generating synthetic but realistic chest X-ray images, GANs improve the training dataset, leading it to improvement in the model's robustness and generalization. The comparative analysis and study of both the models in enhancing pneumonia detection accuracy, provides critical points into the strengths and limitations of each approach. The outcomes of this comparative assessment not only offer valuable benchmarks for future advancements but also enlighten the way heading towards more reliable and effective pneumonia diagnosis from chest X-ray images. © 2024 IEEE.

关键词： Generative adversarial networks

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Identification and Classification of Intestinal Parasitic Eggs in Animals Through Microscopic Image Analysis 3rd

Identification and Classification of Intestinal Parasitic Eg...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Mishra, Ketan Kavitha, C. Kannan, Devi Department of Computer Science and Engineering Atria Institute of Technology Bangalore India

ISBN: (纸本)9789819986453

Intestinal parasitic infections in animals can cause a range of symptoms, including diarrhea, weight loss, anemia, and malnutrition. This project aims to classify parasitic eggs belonging to the Monezia and Strongyles species using microscopic images from a veterinary hospital. The dataset consisted of stool samples from animals infected with these parasites. Image processing and machine learning techniques, including data preprocessing, feature extraction, and feature selection are used to detect and classify the eggs. The goal was to accurately classify the eggs into either the Monezia or Strongyles species. To achieve the goal, two classification algorithms: K-Nearest Neighbors (KNN) and Decision Tree have been used. Using these algorithms, the model achieved accuracies of 91% and 60%, respectively. However, by incorporating Alyuda NeuroIntelligence for classification, a significantly improved accuracy of 92% was obtained. The results demonstrate that the use of Alyuda NeuroIntelligence in conjunction with machine learning techniques can greatly enhance the accuracy of parasitic egg classification. This approach can aid in the diagnosis and treatment of animal infections, improving animal health and well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection 28

Continuous Attentive Multimodal Prompt Tuning for Few-Shot M...

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28th Conference on Computational Natural Language Learning, CoNLL 2024

作者： Jana, Soumyadeep Dey, Animesh Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891761780

With the steep rise in multimodal content on social media, multimodal sarcasm detection has gained widespread attention from research communities. Existing studies depend on large-scale data, which is challenging to obtain and expensive to annotate. Thus, investigating this problem in a few-shot scenario is required. Overtly complex multimodal models are prone to overfitting on in-domain data, which hampers their performance on out-of-distribution (OOD) data. To address these issues, we propose Continuous Attentive Multimodal Prompt Tuning model (CAMP), that leverages the prompt tuning paradigm to handle few-shot multimodal sarcasm detection. To overcome the siloed learning process of continuous prompt tokens, we design a novel, continuous multimodal attentive prompt where the continuous tokens intricately engage with both image and text tokens, enabling the assimilation of knowledge from different input modalities. Experimental results indicate that our method outperforms other multimodal baseline methods in the few-shot setting and OOD scenarios. Our few-shot dataset and code is available at https://***/mr-perplexed/camp. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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EfficientNetB1 Model for Lung Cancer Detection using Biopsy Images

EfficientNetB1 Model for Lung Cancer Detection using Biopsy ...

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

作者： John, Jess Deotale, Aaradhya Fernandez, Figo Jadhav, Dipti Don Bosco Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350382693

Lung cancer, a pervasive and life-threatening ailment, necessitates early and precise diagnosis. This research endeavors to employ Convolutional Neural Networks (CNNs) to automate the detection of lung cancer in histopathological biopsy images, addressing challenges associated with timely and accurate diagnosis. Given the error-prone and time-consuming nature of manual assessment by pathologists, an automated approach becomes imperative. The study encompasses a comprehensive scope, including enhancing interpretability, classifying specific lung cancer subtypes, real-time intraoperative analysis, and extending the application to other cancer types. The methodology involves the utilization of a pre-trained Efficient Net-based model for image classification, showcasing its efficacy in discerning between benign and malignant lung cancer cells, as evidenced by robust training results. Moreover, the model exhibits potential for personalization in lung cancer diagnosis and treatment. Research findings affirm that machine learning models, specifically the EfficientNet-based architecture, markedly improve lung cancer detection accuracy. The model's proficiency in subtype differentiation and its capacity for real-time surgical analysis represent significant strides in lung cancer diagnostics and treatment. In conclusion, this project addresses the critical imperative for accurate and timely lung cancer diagnosis, providing a promising advancement in combatting this devastating disease. The developed model holds transformative potential in radiology and oncology, serving as a valuable tool for medical professionals and contributing to enhanced patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Efficient and Precise CNN based Machine Leaming Method for Microwave Medical Imaging in Cancer Detection 2

Efficient and Precise CNN based Machine Leaming Method for M...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Sunil, B. Sameer, S. Vaseekaran, K.V. Rishi, R. Department of Computer Applications SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India

ISBN: (纸本)9798331504960

Cancer detection is critical for early diagnosis and treatment, and non-invasive imaging methods like microwave imaging have shown promise in this domain. This research presents a CNN-based machine learning model to improve the speed and accuracy of microwave medical imaging for cancer detection. Leveraging large datasets, advanced preprocessing techniques such as noise removal, normalization, and data augmentation were applied. The proposed model includes a multi-layer CNN architecture, optimized using stochastic gradient descent (SGD) and categorical cross-entropy loss functions. Evaluation metrics, including accuracy, sensitivity, and specificity, were calculated from a confusion matrix. The system achieved 95.6% accuracy, 94% sensitivity, and 96% specificity, outperforming traditional models both in terms of accuracy and computational efficiency. The results suggest that the proposed model is well-suited for real-time clinical applications. Future work will focus on expanding the dataset and integrating multimodal imaging to enhance diagnostic precision. © 2024 IEEE.

关键词： Stochastic models

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IndiSentiment140: Sentiment Analysis Dataset for Indian Languages with Emphasis on Low-Resource Languages using Machine Translation

IndiSentiment140: Sentiment Analysis Dataset for Indian Lang...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Kumar, Saurabh Singh, Sanasam Ranbir Nandi, Sukumar Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891761148

Sentiment analysis, a fundamental aspect of Natural Language Processing (NLP), involves the classification of emotions, opinions, and attitudes in text data. In the context of India, with its vast linguistic diversity and low-resource languages, the challenge is to support sentiment analysis in numerous Indian languages. This study explores the use of machine translation to bridge this gap. The investigation examines the feasibility of machine translation for creating sentiment analysis datasets in 22 Indian languages. Google Translate, with its extensive language support, is employed for this purpose in translating the Sentiment140 dataset. The study aims to provide insights into the practicality of using machine translation in the context of India’s linguistic diversity for sentiment analysis datasets. Our findings indicate that a dataset generated using Google Translate has the potential to serve as a foundational framework for tackling the low-resource challenges commonly encountered in sentiment analysis for Indian languages. © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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Weighted Ensemble Learning for Accident Severity Classification Using Social Media Data

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SN computer Science 2024年第5期5卷 1-20页

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, D.V.L.N. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal 506004 India Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Andhra Pradesh Kurnool 518002 India

Due to the heavy increase in social media usage, like Twitter, there is a growing interest in the research community in developing automation tools like accident severity-based tweet classification models. These tools aid in automatically extracting severity information from the accident tweet content. Moreover, prediction models are essential for predicting the severity of an accident to increase the safety efficacy of the road traffic system. However, the difficulty lies in the collection of sufficient labeled data. We propose a model called weighted ensemble-based self-training with decision tree (WESTDT), a semi-supervised methodology with a dynamic data labeling strategy. The base classifier in this model is a homogeneous weighted ensemble of decision tree classifiers for better prediction of pseudo labels. We also propose a novel performance measure called risk factor to estimate the amount of risk present in the application using the prediction model. The proposed model outperformed the state-of-the-art model, namely reliable semi-supervised ensemble learning (RESSEL), and the baseline models, namely decision tree (DT) and self-training with decision tree (STDT), in terms of both the traditional and the proposed measures. The results indicate that the proposed framework outperforms all other models on all datasets in terms of precision, recall, and accuracy by a range of 5–18.3%, 3.9–9.3%, and 6.6%, respectively. These findings can be helpful for the development of efficient and sustainable systems for traffic management and safety. It is also crucial for assisting government authorities in devising prompt, proactive strategies to prevent traffic accidents and enhance road safety. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accident severity classification Decision tree Self-training Semi-supervised learning Weighted ensemble learning

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Multi-Privacy Level Encryption Technique for Secure Cloud-Based Data Repositories

Multi-Privacy Level Encryption Technique for Secure Cloud-Ba...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sunil Kumar Aithal, S. Rajashree Nmam Institute of Technology Department of Computer Science and Engineering Nitte India

ISBN: (纸本)9798350359688

Cloud security encompasses the deployment of advanced technologies, methodologies, and programming to safeguard cloud-based applications, ensuring their protection against potential threats and vulnerabilities. Cloud computing is an environment for providing services namely platform, software, hardware and infrastructure on user demand posing a serious challenge for enabling high secure digital data cloud repository. To keep sensitive information safe in cloud computing, we must use strong techniques to protect it from external attackers. In today's world, creating a security system that will last a lifetime is extremely difficult, as attackers gain more ways to access data as time progresses. Hence, we create a web based application that uses several encryption levels, including the DNA algorithm, Vigenere Cipher, and Advanced Encryption Standard (AES) algorithm to achieve greater confidentiality before loading a digital data onto cloud storage. © 2024 IEEE.

关键词： Cloud security

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