检索结果-内蒙古大学图书馆

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Patel, Harvendra Kumar Rai, Vijendra Singh, Hare Ram Kumar, Rakesh Iimt College of Engineering Greator Department of Computer Science and Engineering Noida India Iec College of Engineering and Technology Greator Department of Computer Science and Engineering Noida India Gniot Noida India

ISBN: (纸本)9798331508685

Body Language decoding is an important aspect of understanding human emotions, intentions, and personality by interpreting non-verbal cu es. It helps to get insights into human behavior, mindset, and psychology of the person. AI Body Talk is a software system that is capable of detecting and interpreting the body language of a person in real-time inference. It can recognize the sign language along with face, hand, and pose estimation with immediate feedback. It empowers the minority group of society who use sign language to communicate in society equipped with the Mediapipe to train custom models with its pre-built solutions to detect face landmarks, hand-tracking, and pose estimation. The results exemplify the system's potential for various applications like monitoring audience distraction, inspecting interviewee personality, understanding cultural sensitivity, diagnosing patients' health, and more. © 2025 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Crop Yield Prediction using Machine Learning 15

Crop Yield Prediction using Machine Learning

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

TRS Scheduling for Improved QoS Performance in Cloud System

引用

computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

来源：评论

学校读者我要写书评

暂无评论

VEye-AI Vision Assistant 10

VEye-AI Vision Assistant

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Breast Cancer Image Classification: Leveraging Deep Learning...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Unmasking the Abnormal Events in Videos Using Deep Learning

Unmasking the Abnormal Events in Videos Using Deep Learning

引用

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

来源：评论

学校读者我要写书评

暂无评论

Iot Based Adaptive Street Lighting System for Rural Area 4

Iot Based Adaptive Street Lighting System for Rural Area

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：