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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Aryan Titoriya, Ankit Kumar Singh, Maheshwari Prasad SRM Institute of Science &Technology Computer Science and Engineering Chennai India National Institute of Technology Computer Science and Engineering Patna India

ISBN: (纸本)9798350365016

Histopathological images serve as pivotal assets within the domain of breast cancer diagnosis, demanding profound comprehension for precise interpretation. This paper introduces a histopathological image classification framework employing the Patch-Based Intuitive Prototype Network (PIP-Net). PIP-Net finds important features without the need for manual part annotations by using prototype patches and self-supervised learning mechanisms. Consequently, PIP-Net adeptly discerns novel patterns within images, thereby facilitating informed therapeutic interventions. Leveraging the 'BrakeHis' dataset, encompassing histopathological images of breast cancer, this paper evaluates PIP-Net's efficacy across varying magnification levels, specifically at 40x, 100x, 200x, and 400x. The classification accuracies achieved are 98.49%, 94.53%, 99.33%, and 98.08% for each respective magnification level. The integration of prototypical elements within images serves to enhance correlation with human visual perception. This shows the paper's dual commitment to not only attaining superior classification performance but also aligning with interpretability and intuitive principles integral to medical image analysis. © 2024 IEEE.

关键词： Self-supervised learning

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The detection of mental health conditions by incorporating external knowledge

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Journal of Intelligent Information Systems 2023年第2期61卷 497-518页

作者： Lin, Yun Sheng Tai, Liang Kuang Chen, Arbee L.P. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Mental health conditions have become a growing problem;it increases the likelihood of premature death for patients, and imposes a high economic burden on the world. However, some studies have shown that if patients are detected and treated early, the social impact and economic costs of mental illness can be reduced. With the popularity of social media, people are sharing their feelings on it, which allows data from social media to be used to study mental health conditions. However, past research had been limited to the optimization of the model or using different types of data available on social media, resulting in models that only rely on data to make decisions. Moreover, people judge things not only by the data collected, but also by background knowledge. Therefore, we considered the diagnostic process of doctors and combined the knowledge of psychological screening tools and diagnostic criteria into the model. In addition, we also tested the effect of combining general knowledge. We retrieve the top m most relevant knowledge segments for each user’s post, and then put both into the prediction model. Experimental results show that our method outperforms previous studies, and the F1-score is increased more than 10% in some situations. Moreover, because the knowledge segments are automatically retrieved, our method does not require additional manual labeling, and the knowledge set can be freely adjusted. These show that our method can help detect mental health conditions and can be continuously optimized in practice. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Diseases

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Prioritization and offloading in P4 switch integrated with NFV

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Telecommunication Systems 2024年第3期86卷 571-584页

作者： Neha, Farhin Faiza Lai, Yuan-Cheng Hossain, Md. Shohrab Lin, Ying-Dar Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

The architecture of integrating Software Defined Networking (SDN) with Network Function Virtualization (NFV) is excellent because the former virtualizes the control plane, and the latter virtualizes the data plane. As Programming Protocol-independent Packet Processors (P4) become popular, the architecture integrating SDN with NFV may shift from traditional switches to P4 switches. In this architecture, which integrates P4 switch and NFV (P4 + NFV), network functions can be provided in both P4 switches (PNF) and NFV (VNF). Thus, to minimize packet delay, an offloading problem between P4 switches and NFV in this P4 + NFV should be addressed. This paper tackles this offloading problem and figures out the prioritization mechanism between newly arriving packets and packets that require VNF for minimizing packet delay. We model and analyze the P4 + NFV architecture using an M/M/1 queuing model with non-preemptive priority. Also, we propose an optimization solution based on gradient descent to find the optimal offloading probability of going to VNF. Results show that optimal offloading from P4 switch to NFV can reduce the average packet delay from 13.74 to 40.73%, when packets requiring VNF are given higher priority than newly arriving packets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Software defined networking

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Deep Learning Techniques for a Comparative Study of Crop Disease Detection 5th

Deep Learning Techniques for a Comparative Study of Crop Dis...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Soumya Prasad, S. Sampath Kumar, L. Mallem, Sai Nirupam Gutta, Hemanth Ahmed, Rafeeq Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India Computer Science and Engineering Government Engineering College West Champaran Bettiah India

ISBN: (纸本)9789819761050

Agriculture industry can be significantly affected by pests. Deep learning techniques are very useful for detecting various diseases in crops. A dataset consisting of 1530 images was divided into the training set, testing set, and validation set. It consists of labels such as "Healthy", "Powdery," and "Rusty." These labels reflect the conditions of a plant and are used to train the model. The trained models using Deep Convolutional Neural Networks successfully find the relevant crop diseases with a 95% accuracy rate. The early detection of diseases is useful to curb the diseases in the budding stage to avoid the rampant onset of plant diseases which prove fatal to the agricultural field. By implementing this strategy, crop loss can be reduced significantly. In this paper, a detailed explanation of various CNN architectures such as Xception, VGG16, Resnet50, DenseNet, efficientNet, and InceptionV3 is outlined. Each model is trained with training data to predict a plant’s disease. The model’s performance is highly dependent on extracting features from the data. The result analysis and visualizations of obtained results are presented in the analysis section of the paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Comprehensive Analysis on Sentiment Using Deep Learning and Transformers 6

A Comprehensive Analysis on Sentiment Using Deep Learning an...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mohsin, Md. Wankhede, Jaishri Fatima, Afshan Solanki, Vijender Kumar Department of Computer Science and Engineering Hyderabad India Stanley College of Engineering & Technology for women Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505790

Emotions describe the social attachment between the human that are ascendancy by cultural norms, social interactions, and Interpersonal bonds. So in this paper we are represent the application of deep learning models and transformers for emotion analysis on text based data. We are using different Word2Vec approaches for analyze the emotion from the textual data. As we know, Distributed Bag of Words (DBOW), Distributed Memory Concatenation (DMC), Distributed Memory Mean (DMM), and hybrid models DBOW+DMC and DBOW+DMM that's all are the part of Word2Vec model and all are based on deep learning techniques. In this paper we also explore the NLP transformer such as BERT, BART, DistilBERT, RoBERTa, and GPT to analyze the emotion of data and compare the performance of deep learning based technique with transformers based techniques. The aim of the exploration is compare the effectiveness of these models using standard sentiment analysis datasets, assessing performance through accuracy metrics. © 2024 IEEE.

关键词： Distribution transformers

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CNN-Based Machine Learning Model for Disease Prediction in Crops

CNN-Based Machine Learning Model for Disease Prediction in C...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Choudhary, Vijay Thakur, Archana Bhalse, Nisha IPS Academy Institute of Engineering & Science Department of Computer Science & Engineering Indore India School of Computer Science & IT Devi Ahilya University Indore India

ISBN: (纸本)9798331512965

Potato crops are vital to global food security, but they are susceptible to several diseases that hinder growth and yield. Traditional methods of detecting these diseases rely on labor-intensive lab tests and human observation. This research presents an enhanced machine learning model for detecting and categorizing potato leaf diseases, specifically early blight and late blight, using Convolutional Neural Networks (CNN). The model is trained on various datasets of potato leaf images, utilizing techniques such as hyper-parameter tuning, data augmentation, and transfer learning. The proposed model attains an accuracy of 96.11%, significantly improving upon existing methods. Image processing techniques, Segmentation is performed using K-means technique and feature extraction is done by utilizing gray-level co-occurrence matrix, along with multi-class support vector machines for classification, are employed. Additionally, the study shows that CNN performs better than Artificial Neural Networks (ANN) in image classification. The study demonstrates how AI and deep learning technology can be used in agriculture to improve crop management and enable early disease prediction. Experiments have been conducted using the real image dataset. © 2025 IEEE.

关键词： Adversarial machine learning

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Validating ML algorithms for efficient Breast Cancer Prediction using K-fold 7

Validating ML algorithms for efficient Breast Cancer Predict...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Refonaa, J. Shabu, S. L. Jany Karthik, Janapati Vardhan, A. Harsha Suresh, L. Padma Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

Breast cancer has always been a disease that affects a lot of people. There is vast range of factors that may lead to breast cancer and this makes it more difficult to detect at an early stage. The tissues called benign or malignant present in the breast profile are so identical that it makes detecting cancer very complex for the doctors. Performing various tests like uniformity of cell shape and thickness of tissues may result in no valuable insights. This gives rise to machine learning and artificial intelligence that can be used in the prognosis of breast cancer. Here we attempt at correlating diverse machine learning algorithms to effectively predict breast cancer over a dataset. The five diverse machine learning algorithms employed are Naïve Bayes, Decision Tree, Neural Networks (Multilayer Perceptron), SVM and K-nearest Neighbour. An effective comparative study has been done to filter out the best Algorithm based on their Performance. © 2024 IEEE.

关键词： Lung cancer

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On Neutrosophy logic for Multiple Correlation Analysis in Fuzzy Uncertain Database

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Neutrosophic Sets and Systems 2025年 76卷 1-17页

作者： Wazir, Samar Beg, M.M. Sufyan Ahmad, Tanvir Sohail, Shahab Saquib Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Computer Engineering Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India

The primary goal of this paper is to introduce a novel method for mining frequent and interesting items by incorporating correlation analysis between two items in an uncertain transactional database using the OWA operator. The work is further expanded by proposing two additional methods for mining frequent and interesting items, utilizing fuzzy means and the OWA operator along with multiple correlation analysis for more than two items in uncertain transactional databases. The effectiveness of the proposed methods is evaluated by running the algorithms on both standard and example datasets, with results compared to the traditional probabilistic approach for identifying frequent and interesting items through multiple correlation. While multiple correlation analysis highlights the relationships of interestingness and uninterestingness between items, the OWA operator enhances the results when combined with fuzzy means and probabilistic methods. Additionally, the paper suggests a future research direction using Neutrosophy logic, which is anticipated to open new avenues for further exploration in this field. © 2025, University of New Mexico. All rights reserved.

关键词： Database systems

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Radio Resource Scheduling in 5G Networks Based on Adaptive Golden Eagle Optimization Enabled Deep Q-Net

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SN computer science 2024年第5期5卷 1-11页

作者： Shilpa, V. Ranjan, Rajeev School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Applications REVA University Bangalore India

The growth of fifth-generation (5G) broadband wireless systems presents several issues in terms of network resource allocation. In a collaborative network of mobile devices, the users, and devices are struggled with precious resources. Consequently, it emphasizes the importance of fair and effective resource allocation for the optimum functioning of the networks. Hence, this research presents the Adaptive Golden Eagle optimization based Deep Q Net (Adaptive GEO_DQN) in radio resource scheduling of 5G networks. Moreover, the 5G network is made up of base stations (BS) and user equipment (UEs). The radio resource scheduler at the BS is active in every slot. The BS can collect the data from the UE, like channel feedback data, buffer, hybrid automatic repeat request (HARQs), and allocation log. The resource blocks (RBs) from the current resource blocks group (RBG) have been scheduled by UEs in the current slot. Furthermore, the DQN is used in the UE scheduling, and the Adaptive GEO is utilized in the training of DQN. In addition, the efficacy of the system is validated with respect to the throughput and fairness metrics with the outcomes of 0.921 Mbps, and 0.902 are attained. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep Q Net (DQN) Fifth generation (5G) Golden eagle optimization (GEO) Radio resource scheduling Resource blocks group (RBG)

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An Efficient Multilingual Text Classification using IndicCorp dataset 5

An Efficient Multilingual Text Classification using IndicCor...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Madanbhavi, Lalitha Desai, Padmashree Sirur, Neha Dhirendra Deshpande, Ananya Hiremath, Risheek V. Patil, Chetan M. Kle Technological University School of Computer Science and Engineering Hubballi India Rv College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Language detection is a crucial preprocessing step in natural language processing (NLP) tasks, especially in a multilingual environment. This paper presents a language detection system utilizing the Naive Bayes classifier, applied to the IndicCorp dataset, which includes a diverse range of languages prevalent in the Indian subcontinent. The IndicCorp dataset provides a rich source of textual data across multiple Indian languages, enabling the development of robust and accurate language models. Our system leverages the simplicity and effectiveness of the Naive Bayes algorithm, known for its high efficiency in text classification tasks. The proposed approach involves pre processing the dataset, extracting features using the CountVectorizer, and training the Naive Bayes classifier to identify the language of a given text. Experimental results demonstrate the system's capability to achieve an accuracy of 73.37% in language detection across various Indian languages within the IndicCorp dataset. This performance underscores the system's effectiveness in classifying languages like Hindi, Bengali, Tamil, and others, highlighting its potential for broader NLP applications and multilingual content processing. The necessity for multilingual classification arises from the need to support diverse linguistic communities and facilitate seamless communication. Accurate language detection enables various downstream applications such as machine translation, sentiment analysis, and information retrieval, enhancing user experience and accessibility. This study emphasizes the practical application of Naive Bayes for language detection and underscores the value of the IndicCorp dataset in developing language processing tools tailored to India's linguistic landscape. The achieved accuracy demonstrates our approach's robustness in handling diverse linguistic characteristics and showcases its potential impact on communication and information retrieval in multilingual contexts. © 2024 IEEE.

关键词： Data assimilation

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