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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Prathyakshini Shetty, Jyothi Department of Information Science and Engineering India Department of Computer Science and Engineering India

ISBN: (纸本)9798350306637

The rapid growth of email usage has brought about an unprecedented increase in spam emails, posing serious challenges to users and businesses alike. To tackle this issue, this study presents an enhanced spam email detection system leveraging the power of machine learning techniques. The objective is to create a robust and efficient model capable of accurately differentiating between legitimate emails and spam messages. Various features are extracted from the email content, including text-based characteristics, header information, and metadata, to create comprehensive representations of the emails. Machine learning algorithms, such as KNeighbors, Support Vector Classifier, Multinomial NB, Logistic Regression, Random Forest, Extra Trees Classifier, AdaBoost, Bagging, XGB Classifier, Decision Tree, Gradient Boosting, Gaussian NB (Naïve Bayes), Multinomial NB, Bernoulli NB (Naïve Bayes) and ensemble model are employed to develop and compare different spam detection models. The experimental results demonstrate the efficacy of the proposed enhanced spam email detection system. The combination of machine learning techniques leads to significant improvements in accuracy and efficiency, enabling effective identification and classification of spam emails. Bernoulli achieved better accuracy of 99%. © 2023 IEEE.

关键词： Adaptive boosting

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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Realm of Medical Image Processing Under the Light of Artificial Intelligence 1

Realm of Medical Image Processing Under the Light of Artific...

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1st International Conference on Healthcare Innovations, Software and engineering.Technologies, HISET 2024

作者： Tamboli, Asif I. Shah, Samarth Saxena, Gaurav Wagh, S.J. Prashanth, Addagatla Diallo, Mamadou Yero Department of Radio-Diagnosis Karad India Chandigarh University Department of Computer Science Mohali India Government Engineering College Department of Computer Science Karad India Institute of Aeronautical Engineering Electronics and Communication Engineering Hyderabad India Sharda University Computer Science & Engineering Gr.Noida India

ISBN: (纸本)9798350360707

Over the previous century, a fundamental breakthrough in medical imaging processing as well as storage occurred, with the switch from traditional detectors in radiography to digital detectors. As a result, medical imaging digitalization constitutes a big achievement and promises considerable breakthroughs in cancer. Thus, goal of study is to analyze the advanced imaging process in medical field using artificial intelligence (AI). AI, on the other hand, has the potential to go beyond these applications by producing picture-based biomarkers that are able to accurately predicting survival or response to therapy, thereby extracting far more information from photographic images than used to be conceivable. Such biomarkers might be incorporated into existing prognostic as well as predictive models used in clinical treatment, functioning as integrative biomarkers. So, the study helps in understanding importance of medical imaging processes and further helps in diagnosing minor diseases and eliminates it without human intervention with help of AI. © 2024 IEEE.

关键词： Medical image processing

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An ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis

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International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 241-261页

作者： Sharanabasappa Suvarna Nandyal Computer Science and Engineering Department Sharnbasva University KalaburagiGulbargaIndia Computer Science and Engineering Department Poojya Doddappa College of Engineering KalaburagiGulbargaIndia

Purpose-In order to prevent accidents during driving,driver drowsiness detection systems have become a hot topic for *** are various types of features that can be used to detect *** can be done by utilizing behavioral data,physiological measurements and vehicle-based *** existing deep convolutional neural network(CNN)models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or ***,the developed model suffered from the limitation of high computational cost because of the application of approximately 140million ***/methodology/approach-The proposed model uses significant feature parameters from the feature extraction process such as ReliefF,Infinite,Correlation,Term Variance are used for feature *** features that are selected are undergone for classification using ensemble ***-The output of these models is classified into non-drowsiness or drowsiness *** limitations/implications-In this research work higher end camera are required to collect videos as it is ***,researches are encouraged to use the existing *** implications-This paper overcomes the earlier *** developed model used complex deep learning models on small dataset which would also extract additional features,thereby provided a more satisfying ***/value-Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

关键词： Correlation Driver drowsiness Ensemble learning mode Infinite ReliefF Term variance

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Effect of Vaccination on Stability of Wireless Sensor Network Against Malware Attack: An Epidemiological Model

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SN computer science 2024年第2期5卷 1-13页

作者： Verma, Chakradhar Gupta, C.P. Department of Computer Science and Engineering Rajasthan Technical University Rajasthan Kota 324010 India

Wireless sensor network (WSN) has gained increased prominence due to fast deployment of sensors in IoT. With WSN being deployed in varying fields viz. industrial automation, surveillance, security in these networks has become important consideration. Security solutions employed in infrastructure networks are unsuitable for resource constrained WSNs. To limit power consumption within the node, implementing antivirus/ malware protection within the node is not recommended. However, WSNs are equally prone and vulnerable to such threats (malware, worm, virus etc.). Due to distributed topology and decentralized network control, malware detection and isolation of infected nodes needs to be carried out within the network. Techniques such as SIS (Susceptible–Infected–Susceptible), SIR (Susceptible–Infected–Recovered), SCIR (Susceptible–Carrier–Infectious–Recovered) have been proposed for malware detection in WSNs. However, the techniques suffer from quarantine and vaccination. In this paper, we propose an epidemiological model for immunizing the network against such attacks. In our method, the nodes are initially vaccinated and then deployed. This protects the malware infection after deployment. The model also has a quarantine state. A vaccinated node getting infected after deployment is placed in this state. Simulation results have shown lower probability of malware attack. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Basic reproduction number Coverage and connectivity Malware Points of equilibria Stability Wireless sensor network

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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2nd International Conference on Machine Learning and Data engineering. ICMLDE 2023

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Real-Time Crop Monitoring System Using Deep Learning Techniques for Increasing Production

Real-Time Crop Monitoring System Using Deep Learning Techniq...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Dutta, Chiranjit Moudgil, Suveg Aluvala, Srinivas Saxena, Virat Raj Devi, K. Singh, Jagendra SRM Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Galgotias University School of Computer Science Engineering Greater Noida India SR University School of Computer Science & Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India DAV Autonomous College Department of Commerce Titilagarh India Bennett University School of Computer Science Engineering & Technology Greater Noida India

ISBN: (纸本)9798350394412

This study applies machine learning to determine rice crop production using sensor information from temperature, humidity, and water levels. This project looks forward to providing insights to maximize agricultural practices and allow data-driven decision-making in rice cultivation. This is in line with how well these disparate machine learning models can predict rice crop production when measured against precision, accuracy, recall, and F1 score. To some extent, this is evident, as this study found that the machine learning models can predict yield quite satisfactorily, of which the model Random Forest performs very well in terms of precision and accuracy. Among them were the exactitude of sensor data and the algorithm selection, along with subsequent model assessment, refinement, and the ingression of great datasets inclusive of a broader set of environmental characteristics and crop yields - that were considered to be crucial in improving the precision of yield prediction algorithms. Additionally, the use of machine learning in farming has a number of benefits, such as enabling farmers to make decisions about crop management, irrigation, and optimization of yield. Combining machine learning methodologies with sensor technology can optimize resource allocation, save water, and improve overall agricultural productivity. Further, this study will facilitate the implementation of smart irrigation systems and set the stage for data-driven decision-making in agriculture for better farming practices and food production worldwide. © 2024 IEEE.

关键词： Smart agriculture

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GSM based LPG Leakage Detection and Simulation System 2

GSM based LPG Leakage Detection and Simulation System

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Ramanan, K. Vashali, S. Nanthini, S. Thaqfeen, M. Hifaya Sivaraja, P.M. Maheswari, G. Uma Npr College of Engineering and Technology Department of Computer Science Engineering TamilNadu India Amrita College of Engineering Department of Computer Science Engineering TamilNadu India Rmk College of Engineering and Technology Department of Computer Science Engineering TamilNadu Chennai India

ISBN: (纸本)9798350347579

In a time when LPG is increasingly used as a fuel source for factories, houses, and cars, any accidental releases would have devastating consequences. Detectors for LPG leaks should be placed in potentially hazardous places as a precaution. Therefore, the purpose of this study is to develop an LPG leak detection and reaction system using a microcontroller. The MQ-6 gas sensor is used to detect leaks, and the user is notified by GSM-based text message and an audible alarm. LPG supply was cut off to reduce waste and avoid mishaps, which was a primary function of the system. This was accomplished by consulting datasheets while deciding which components to include in the circuit design, and by running simulations of the circuit in Proteus to check its viability. When the amount of LPG in the air reaches a certain level, the system sends a text message to a user-specified cell phone number stored in the microcontroller and concurrently triggers the warning buzzer and displays the data on an LCD. The LPG supply may be cut off by sending a signal to the solenoid valve. This setup notifies you of a gas leak and immediately cuts off the gas supply, so you don't waste any fuel and don't risk any mishaps. LPG vendors and consumers alike will benefit greatly from this approach. © 2023 IEEE.

关键词： Microcontrollers

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Analysis of SiGe, InGaAs and GaN on 5nm MOSFET Modelling with Applications 5

Analysis of SiGe, InGaAs and GaN on 5nm MOSFET Modelling wit...

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5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Chakraborty, Raktim Mandal, Jyotsna Kumar University of Kalyani Department of Computer Science and Engineering Kalyani India Raiganj University Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350347265

A 5nm MOSFET, CMOSFET and an Inverter circuit has been presented. The present work has deployed three different semiconductor agents and the analysis has been furnished in this paper. Silicon Germanium, Indium Gallium Arsenide and gallium nitride has been deployed and it has been found that silicon germanium is performing with optimal threshold voltage of 0. 1759V and providing maximum value of leakage current among of the three semiconductor candidates i.e. 601.1 x10-10A/um. Indium Gallium Arsenide is delivering maximum drive current 40.34 x10-7A/um among of the three other channel materials when a drain voltage is given of 0. 0005v. As an application of the 5nm Silicon Germanium based MOSFET, a CMOSFET and an Inverter circuit has been furnished. © 2023 IEEE.

关键词： III-V semiconductors

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CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models

CiGeN 1.0: A Generative AI Enabled Medical Image Augmentatio...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Dash, Archana Nayak, Debasish Swapnesh Kumar Swarnkar, Tripti Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Bhubaneswar751030 India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India National Institute of Technology Department of Computer Application Raipur492010 India

ISBN: (纸本)9798331530259

Deep Learning methods have gained a significant momentum in medical image classification tasks, including many ranges of datasets. However, these datasets often suffer from class imbalance, where the minority class (e.g., diseased cases) is underrepresented. This imbalance leads to biased predictions, affecting the model's performance. Generative Adversarial Networks (GANs) offer a potential solution by generating synthetic images for the minority class, helping balance the dataset. Methodology: In this study we further evaluated our Deep Learning Model CoVaD-GAN with separate set of datasets to test the dynamics behind it. We applied a Deep Convolutional GAN (DCGAN) to generate synthetic images for underrepresented classes in the chest X-ray (CXR) and Break His histopathological dataset. Augmented synthetic images were made a part of training set, and the model's performance was evaluated before and after augmentation using VGG-16, ResNet-50 with necessary metric measures as accuracy, sensitivity, and specificity were compared pre- and post-GAN augmentation. We also evaluated the quality of GAN-generated images using Fréchet Inception Distance (FID) and manual expert reviews. The results demonstrate significant improvements in model accuracy, sensitivity, and F1-scores after applying GAN-based augmentation. The use of GANs for data augmentation enhances the ability of deep learning models to generalize, particularly for minority classes. GAN-generated images were of high quality and contributed positively to model performance. The Proposed model (CiGeN 1.0) dynamically generalizes the other medical dataset introduced to it and derived a fair resulting diagnosis. © 2025 IEEE.

关键词： Chest X-rays CNN Deep Learning GAN Medical data augmentation

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