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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tony, Akash Lalkumar, Dona Saint, Luke Mohammed Hazim, A.S. Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India

ISBN: (纸本)9798331300579

As the world faces growing challenges in ensuring food security, predicting crop yields accurately be- comes increasingly crucial. Traditionally, this prediction re- lied on methods limited by their inability to handle complex interactions between various factors like climate, soil, and farming practices. This is where Machine Learning (ML) emerges as a game-changer. By harnessing the power of data-driven insights, ML offers highly accurate predictions surpassing traditional methods. This paper specifically dives into the potential and challenges of utilizing ML for crop yield prediction, highlighting its strengths and areas for further development. While traditional approaches often fall short in capturing the intricate relationships impacting yield, the proposed method leverages satellite imagery to extract valuable features and deliver precise predictions on granular levels. Ultimately, this paves the way for farmers to make informed decisions, optimize resource allocation, maximize production, and contribute significantly to global food security. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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High-Resolution Data-Driven Approach for Pedestrian Detection Using Deep Learning 2

High-Resolution Data-Driven Approach for Pedestrian Detectio...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Selva Lakshmi, C.B. Dharshanaa, S. Dhanavidhya, C. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350349221

Pedestrian detection is one of the primary objectives in the field of computer vision demonstrating versatile applications in various domains including pedestrian monitoring in surveillance systems and driverless automobile assistance. Since the surveillance cameras are set high to capture a wide range of areas, they produce small and noisy images. When these footage are given as input, the extracted features of the pedestrian images produce an inferior detection rate. To address the above issue, this research suggests a solution employing stationary wavelet dilated residual (SWDR) networks that significantly enhance edge details of the low-resolution images thus improving the efficiency of the pedestrian detection system. © 2024 IEEE.

关键词： Wavelet transforms

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A Chatbot Assistant for Early Detection and Management of Sugarcane Diseases 2

A Chatbot Assistant for Early Detection and Management of Su...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Sarala, R. Vishnu Varthini, A. Swetha Raji, T. Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

The Poaceae family's sugarcane, which has a high sucrose content, is an important resource used to make jaggery, white sugar, and byproducts like bagasse and molasses. Meanwhile, these Sugarcanes are getting largely affected and unproductive due to diseases making early detection crucial. This paper has bring forward a deep-learning framework which identifies the vitality of sugarcane plants by examining their physical traits. The market has foreseen a huge demand for Sugarcane plants therefore examining sugarcane diseases is crucial and the main hurdle for this is posed by varying monsoon seasons and other climatic changes. To tackle this hurdle, the authors put forward a solution which includes examining the staging of some pre-trained deep learning models, specifically VGG19 and ResNet50 to detect disorder in Sugarcane plants. Error rates and Performance metrics are checked for measuring overall efficiency. A graphical analysis comparing the models based on their sensitivity is also included. © 2024 IEEE.

关键词： Plant diseases

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VEye-AI Vision Assistant 10

VEye-AI Vision Assistant

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Joy, Alen Nazeer, Naja Abdul Gregory, Neha Maria Davis, Jasmy Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350384369

This paper aims to deal with the difficulties faced by blind while interacting with their surroundings. The proposed system introduces VEye - An AI Vision Assistant, a wearable device with an integrated camera and an ear-piece, using AI algorithms to convert real-time visual input from camera to audio description. It includes other features such as obstacle detection, text recognition, face recognition for authorized individuals, object identification, GPS integration and an emergency button. This device assists the visually challenged people in their day- to-day activities and develop a feeling of independence in them. © 2024 IEEE.

关键词： Obstacle detectors

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A comprehensive review on detection of depression through social media analysis 4th

A comprehensive review on detection of depression through so...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Dogra, Shriya Kaur, Parneet Sharma, Tejpal Kumari, Nisha Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781032911571

Social media platforms like Instagram, Twitter, and Facebook have completely changed our world. People today exhibit a kind of digital character and are more linked than ever. While social media undoubtedly offers many noteworthy advantages, there are also unavoidable drawbacks. Later Research has demonstrated a link between elevated use of social media platforms and heightened levels of depression. The goal of the current study is to study the existing machine learning approaches to identify potentially sorrowful Twitter users based on their tweets and network behaviour. To accomplish this, we assessed classifiers that identify whether or not a person is depressed based on information extracted from their tweets and network behaviour. It is observed that as a greater number of features are incorporated, the F-score and accuracy in classifying depressed persons also improve. This technique is a predictive, data-driven tool for early mental health diagnosis of depression or any type of psychiatric ailments. The main contribution of the article is the examination of the traits and how they affect the ability to gauge depression severity. © 2025 the Author(s).

关键词： Tweets

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GuardianForest: Cloud-Powered Patient Health Assurance through Isolation Forests 7

GuardianForest: Cloud-Powered Patient Health Assurance throu...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Subramanian, M. Gokulakrishnan, K. Nirmala, B. Vijaya Vijay Shankar, S. Krishnan, R. Santhana Vinoth Rajkumar, G. Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India College of Engineering Guindy Anna University Department of Electronics And Communication Engineering Chennai India Psna College of Engineering And Technology Department of Information Technology Dindigul India Sona College of Technology Department of Electrical And Electronics Engineering Salem India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India J.P. College of Engineering Department of Electronics And Communication Engineering Thenkasi India

ISBN: (纸本)9798350359299

The proposed patient monitoring system is a substantial progression in healthcare technology. It integrates various sensors and communication modules to provide real-time patient data to caregivers and medical professionals. The array of sensors includes a non-contact temperature sensor, a rain sensor, a Max30100 sensor, and a load cell sensor. The non-contact temperature sensor vigilantly tracks the patient's body temperature, alerting caregivers to any deviations. The rain sensor serves as a communication channel for patients unable to express needs, detecting urination. The Max30100 sensor monitors critical health metrics like heart rate and blood oxygen levels for early detection of conditions. The load cell sensor observes saline levels in IV bags, triggering alerts on nearing critical thresholds. Powered by an Arduino microcontroller, the system aggregates data, relaying it to a dedicated server via Wi-Fi for remote accessibility. The Isolation Forest-based IsoSentry Algorithm enhances anomaly detection, identifying aberrations in health readings. Adding reliability, a GSM module connected to Arduino dispatches real-time alerts via text messages. In essence, "GuardianForest"epitomizes a transformative leap in healthcare, integrating cutting-edge hardware, wireless communication, and cloud-based data management to enhance overall healthcare responsiveness and efficacy. © 2024 IEEE.

关键词： Internet of things

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Iot Based Adaptive Street Lighting System for Rural Area 4

Iot Based Adaptive Street Lighting System for Rural Area

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Selvalakshmi, C.B. Raja Alagurishi, N. Aravind, T. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350393354

Adaptive street lighting system presents an innovative energy-efficient smart lighting technique that leverages advanced technologies, including Internet of Things (IoT) sensors and adaptive control algorithms. This introduces a cutting-edge energy-efficient smart street lighting technique designed to address the growing need for sustainability and energy conservation in rural environments. The proposed system utilizes a network of sensors to detect real-time environmental conditions, such as ambient light levels and pedestrian or vehicular presence. Based on this data, an adaptive control algorithm dynamically adjusts the brightness of streetlights, ensuring they provide adequate illumination while minimizing energy consumption. Depending upon number of vehicles passed during night, streetlights are operated at four different intensities that is from 20% to 50% with the step size of 10% in the absence of vehicles while operating at 100% intensity upon vehicle's arrival. This dimming operation not only enhances the life span of streetlights but also enables significant conservation of energy up to 84%. This system smartly takes an action based on the presence or absence of vehicles and sunlight via light dependent resistor (LDR), infrared (IR) sensors and Passive infrared (PIR) sensor. © 2024 IEEE.

关键词： Internet of things

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Unmasking the Abnormal Events in Videos Using Deep Learning

Unmasking the Abnormal Events in Videos Using Deep Learning

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Guhapriya, D. Kiruthika, K. Padmadevi, S. Velammal College Of Engineering And Technology Department Of Computer Science And Engineering Madurai India

ISBN: (纸本)9798350352689

Surveillance systems greatly benefit from video-based anomaly detection as it helps to improve security measures and situational awareness. This research paper presents a novel methodology for anomaly detection using deep learning techniques, specifically the Long-term Recurrent Convolutional Network (LRCN). The suggested methodology integrates long short-term memory (LSTM) layers to capture temporal relationships and convolutional neural networks (CNN) to extract spatial features within video sequences. This methodology is evaluated on a diverse dataset encompassing various human activities, both normal and anomalous. Performance metrics, including False Alarm Rate, Area Under the ROC Curve (AUC), and Accuracy, demonstrate the usefulness and superiority of the suggested approach over existing methods. The results highlight the potential of this approach to enhance security and surveillance applications. © 2024 IEEE.

关键词： Anomaly detection

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Breast Cancer Image Classification: Leveraging Deep Learning and Large Language Models for Semantic Integration 2

Breast Cancer Image Classification: Leveraging Deep Learning...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Harini, K.K. Nandhini, R. Rajeswari, A.M. Deepalakshmi, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350383652

Breast cancer, a significant global health problem that is mainly affecting women, demonstrates the importance of early detection to enhance survival rates. Medical image classification is a significant field that utilizes deep learning methods such as Convolutional Neural Networks (CNNs), Region-Based CNNs (R-CNNs), and Deep Neural Networks (DNNs). Despite this, challenges such as over fitting persist, particularly with limited data or complex models. Although these models learn hierarchical features, interpreting them in intricate image structures is still challenging and complicates decision rationale. To address this, we propose a comprehensive approach for breast cancer image classification. The initial process involves image segmentation using CNN to identify regions of interest, followed by precise object detection with OpenCV. Data quality and diversity are ensured by preprocessing and augmentation with ImageDataGenerator, while informative features are extracted using the InceptionV3model. Our analysis of textual descriptions and embeddings is done by integrating Sentence Transformers and CLIP from the Large Language Model (LLM) framework. Achieving robust decision-making is ensured by setting a threshold of 0.7 for image classification based on predicted malignancy probability. Finally, improving the computations of similarity scores can improve prediction accuracy for reliable diagnostic interpretations. An accuracy of 0.92 in breast cancer diagnosis is guaranteed by integrating LLM and CNN for classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Emotion identification in human faces through ensemble of deep learning models

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Journal of Intelligent and Fuzzy Systems 2023年第6期45卷 9729-9752页

作者： Naveen Kmuar, M. Godfrey Winster, S. Department of Computer Science and Engineering Saveetha Engineering College Thandalam India Department of Computing Technologies Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India

Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset. © 2023 - IOS Press. All rights reserved.

关键词： Emotion Recognition

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