International Research Journal on Advanced Engineering and Management (IRJAEM)最新文献_第7页

Human Emotion Detection Using CNN and Transfer Learning 利用 CNN 和迁移学习进行人类情绪检测

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-17 DOI: 10.47392/irjaem.2024.0188

P. Manimohan, C.S. Keerthi, Sunkara Kavya Sudha, Devireddy Mourya Chandra Reddy, C. Mahendra, Raginutala Nagarjuna

{"title":"Human Emotion Detection Using CNN and Transfer Learning","authors":"P. Manimohan, C.S. Keerthi, Sunkara Kavya Sudha, Devireddy Mourya Chandra Reddy, C. Mahendra, Raginutala Nagarjuna","doi":"10.47392/irjaem.2024.0188","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0188","url":null,"abstract":"Facial emotion detection is a critical component in human-computer interaction, mental health assessment, and security systems. In this project, we propose a robust facial emotion detection system leveraging state-of-the-art deep learning techniques. Our system utilizes Convolutional Neural Networks (CNNs) to extract meaningful features from facial images and classify them into five distinct emotional categories: neutral, surprise, sad, happy, and angry. We conducted extensive experiments on a diverse dataset consisting of over 10,000 annotated facial images collected from various sources. Through data augmentation techniques such as rotation, translation, and flipping, we expanded the dataset to enhance model training. Additionally, we employed transfer learning by fine-tuning a pre-trained CNN model, ResNet50, on our dataset to leverage its learned features. This project presents a system for real-time emotion monitoring using computer vision techniques. The system utilizes the Haar Cascade Classifier for face detection in live webcam video streams. Furthermore, we evaluated the system's performance across different lighting conditions, poses, and facial occlusions to assess its robustness in real-world scenarios. Our results indicate that the system maintains consistent performance across diverse conditions, making it suitable for deployment in applications requiring real-time facial emotion recognition.","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":"37 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pest Detection and Classification in Peanut Crops 花生作物的害虫检测与分类

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-17 DOI: 10.47392/irjaem.2024.0189

Sushma D S, Mohammed Alqhama, Aravind M, Jayanth A B, Rakshith Kumar K

{"title":"Pest Detection and Classification in Peanut Crops","authors":"Sushma D S, Mohammed Alqhama, Aravind M, Jayanth A B, Rakshith Kumar K","doi":"10.47392/irjaem.2024.0189","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0189","url":null,"abstract":"Recent advancements in image processing have significantly improved pest detection and classification in peanut crops. Our study introduces an innovative approach that optimizes image features for accurate pest identification. Leveraging insights from successful image analysis methodologies, our model employs a tailored architecture for pest detection, segmentation, and classification tasks. By integrating dual branch segment representations and a dual-layer transformer encoder, we aim to enhance image representations and consolidate pest image segments of varying sizes. We evaluate our approach using three distinct pest datasets—Aphids, Wireworm, and Gram Caterpillar—ensuring comprehensive analysis and model validation. Prior to training, we preprocess the datasets extensively, employing feature extraction techniques and addressing image quality issues. We then apply normalization procedures to standardize the data for seamless integration into our model architecture. Our methodology focuses on extracting key features through self-attention mechanisms and standardized scaling processes to enhance predictive capabilities. Comprehensive experimentation demonstrates the superiority of our approach, outperforming established benchmarks in pest detection and classification with high accuracy rates. In summary, our study presents a novel framework that optimizes feature extraction and enhances predictive accuracy in pest detection and classification for peanut crops, addressing the unique challenges of agricultural pest identification.","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":"9 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Regional Crime Data Analysis and Insights Using Fb Prophet 使用 Fb 先知分析和洞察地区犯罪数据

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-17 DOI: 10.47392/irjaem.2024.0192

Sonali Pakhmode, Vanshita Gaikwad, Bhargavee Talekar, Neha Golatkar, Vipin Jaiswal

{"title":"Regional Crime Data Analysis and Insights Using Fb Prophet","authors":"Sonali Pakhmode, Vanshita Gaikwad, Bhargavee Talekar, Neha Golatkar, Vipin Jaiswal","doi":"10.47392/irjaem.2024.0192","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0192","url":null,"abstract":"Crime is one of the most dominate and formidable aspect of the society. Crimes are committed daily, which has made the lives of the common people restive. Therefore, preventing the crime from occurring can be achieved through a systematic approach by recognizing and evaluating the criminal trends specific to a certain location. Data analysis can assist us to explore former criminal incidents and help us analyze the patterns and hidden correlations which can be used for prevention of crime. Our system aims to analyze crime-prone regions by using the location and time of the crime. Data visualization is a powerful tool for investigating crime rates across the country. Heat maps can be useful for determining the hotspots of crime. The proposed system analyzes the data using the FBprophet model to forecast crime rates in different regions. The Facebook Prophet library, maintained by Facebook, provides forecasting of time series data. This application uses past data and web-scraped data from various sites in order to get detailed studies of various crimes in multiple places. It also enables you to get data about crimes in many places across India by using the pin codes. Government organizations and police departments can effectively utilize it to monitor crime events within a locality.","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":"118 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Innovative Approach for Career Using Career Roadmap Provider 使用职业路线图提供商的创新职业生涯方法

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-17 DOI: 10.47392/irjaem.2024.0193

Ayush Dharmare, Aryan Ramedwar, Kartik Bhujade, Aryan Maurya, Laxmikant Borkar, Snehal Bhujade

{"title":"Innovative Approach for Career Using Career Roadmap Provider","authors":"Ayush Dharmare, Aryan Ramedwar, Kartik Bhujade, Aryan Maurya, Laxmikant Borkar, Snehal Bhujade","doi":"10.47392/irjaem.2024.0193","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0193","url":null,"abstract":"In today’s world a lot of people are a bit confused about the field which they need to choose as their career, and even if some people are clear about their goals, they don’t have an actual path which they need to follow to become a professional in their field. There are several articles available on the internet which will provide you information regarding your career but it is very vast and the user interface is not much attractive and precise for the users. Along with that several new fields are emerging in the world around us about which people are not much aware and even the search engines don’t have much information about it. Hence, we are developing a “Career Roadmap Provider” which is a website that will provide our users a detailed roadmap regarding the particular field which they have searched for. The roadmap will include the field description, different available opportunities within that field, the companies that the user can apply for, required skills and other necessary information. This will clear the confusion that several users are having regarding their particular field by providing them a complete and precise Career Roadmap.","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart Glasses for Visually Impaired People Using IoT 利用物联网为视障人士设计智能眼镜

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-17 DOI: 10.47392/irjaem.2024.0191

Mrs. K. Swathi, P. Gayathri, D. Manasa, G. Pavani, B. Likhitha

{"title":"Smart Glasses for Visually Impaired People Using IoT","authors":"Mrs. K. Swathi, P. Gayathri, D. Manasa, G. Pavani, B. Likhitha","doi":"10.47392/irjaem.2024.0191","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0191","url":null,"abstract":"The visually handicapped have a difficult time getting by in a world that is mostly intended for sighted people. Even seemingly simple things like locating obstacles, traversing new places, and keeping spatial awareness can be difficult for them and frequently impede their freedom and movement. Their sense of dependence and loneliness are exacerbated by the absence of real-time support and systems catered to their individual needs, which exacerbates these challenges. Smart glasses are made especially for those who are blind or visually impaired to address these issues. These smart glasses use technologies to improve the wearer's movement and independence while offering real-time help. It seeks to remove the obstacles that visually impaired people encounter when attempting to safely and successfully navigate their environment by utilizing modern sensor technology and creative IoT solutions. [3] Its primary goal is to create a wearable assistive technology with features including proximity alarms, object detection, distance measuring, and location mapping. It seeks to provide a complete and user-friendly system that caters to the unique needs of visually impaired people by integrating a variety of sensors, communication modules, and cloud-based platforms. The goal of the glasses is to let people to live more independently and confidently by removing the limits imposed by visual impairment, which will ultimately improve their overall quality of life.","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":"120 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Crop Monitoring System Using IOT 利用物联网的作物监测系统

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-15 DOI: 10.47392/irjaem.2024.0185

Nanthini K.S, Sujeetha B

{"title":"Crop Monitoring System Using IOT","authors":"Nanthini K.S, Sujeetha B","doi":"10.47392/irjaem.2024.0185","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0185","url":null,"abstract":"Currently, the low agricultural output is causing farmers to suffer. Low crop growth is primarily caused by knowledge of soil fertility and crop selection, even though proper crop selection is the primary booster key for enhancing agricultural yield by doing soil analysis and taking metrological elements into consideration. Farmers with limited experience in conventional farming are finding it difficult to make wise crop selection choices in the current environment. The low soil fertility is caused by the same crop being chosen for each seasonal cycle. The goal of this research is to use IoT devices to create an accurate and efficient system. Compared to the outdated manual laboratory testing procedures, which are subject to human errors, such a system is predictable. A crop's proper selection is mostly important in the agricultural field. As a contribution, we offer Smart Crop Selection (SCS), an IOT-based approach that is based on soil and metrological factor data. These variables include soil moisture content, temperature, and precipitation. Because these issues are not given as much care as they should, existing IoT-based systems are less efficient than our proposed model. Real-time sensory data is transmitted to Firebase cloud for analysis in the suggested model","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":"64 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domesticating Biogas – A Viable Alternative to LPG in India 沼气家用化--印度液化石油气的可行替代品

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-15 DOI: 10.47392/irjaem.2024.0186

Sunil Shinde, Laxmikant Mangate, Dhruwa Gokhale, Shantanu Dongardive, Aaryash Dugge, Shreyas Gaikwad, Pratik Garware

引用次数: 0

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network 利用深度卷积神经网络检测植物叶片病害的移动系统

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-15 DOI: 10.47392/irjaem.2024.0183

Piyush Hood, Aditi Kawade, Aachal Karpate, Bhagyashri Undre, Archana Bhade

{"title":"A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network","authors":"Piyush Hood, Aditi Kawade, Aachal Karpate, Bhagyashri Undre, Archana Bhade","doi":"10.47392/irjaem.2024.0183","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0183","url":null,"abstract":"Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":"9 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Based X-RAY Prediction Model 基于机器学习的 X 射线预测模型

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-15 DOI: 10.47392/irjaem.2024.0187

Nirdesh Jain, Dr. Aditya Mandloi

引用次数: 0

A Study on Technological Advancements in Cashew Processing Machines in India 印度腰果加工机械技术进步研究

International Research Journal on Advanced Engineering and Management (IRJAEM) Pub Date : 2024-05-15 DOI: 10.47392/irjaem.2024.0184

Ajiesya. D P, Adlin. M, Dr. Babima. M, Jenit Hanson G

{"title":"A Study on Technological Advancements in Cashew Processing Machines in India","authors":"Ajiesya. D P, Adlin. M, Dr. Babima. M, Jenit Hanson G","doi":"10.47392/irjaem.2024.0184","DOIUrl":"https://doi.org/10.47392/irjaem.2024.0184","url":null,"abstract":"This article theoretically analyses about the “A Study on Technological Advancements in Cashew Processing Machines in India.” In addition to being a significant cash crop, cashew is one of the most valuable processed nuts traded on the world's commodity markets. Cashew is grown on 9,23,000 hectares of land in India, where it produces 613,000 MT and has a productivity of 0.7 MT/ha. With a 32.3% share, Maharashtra leads the nation in cashew output. For its kernels, it is extensively grown across the tropics. It is produced in a few plain regions of Karnataka and Madhya Pradesh as well as on the east and west coasts of India. In Maharashtra, productivity is shown to be the highest. With a 25.52% global output share, India is the world's biggest cashew producer. About 230 kg (23%) are created for every metric ton of raw cashew seed produced and processed in India. This study employed frameworks such as the SWOT and PEST analyses to analyses market trends. Though India is one of the leading producers of cashew, still they were using one of the traditional methods to processing the cashew nuts. The study enhances about the marketing strategies which should be followed to develop the cashew processing machines in India.","PeriodicalId":517878,"journal":{"name":"International Research Journal on Advanced Engineering and Management (IRJAEM)","volume":"20 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0