American journal of advanced computing最新文献

Harnessing the Power of Convolutional Neural Network for Exoplanet Discovery 利用卷积神经网络的力量发现系外行星

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21023

None Gaurav, Sumit Gupta

{"title":"Harnessing the Power of Convolutional Neural Network for Exoplanet Discovery","authors":"None Gaurav, Sumit Gupta","doi":"10.15864/ajac.21023","DOIUrl":"https://doi.org/10.15864/ajac.21023","url":null,"abstract":"The discovery of planets apart from Earth that can sustain lives has always been fascinating as well as challenging. Discussion around such planets, popularly termed as \"Exoplanets\" have been doing the rounds for quite some time now. These exoplanets are often considered to be \"Earth-like\" or \"habitable\" because they may have conditions that could potentially support life. This work focuses on how Deep Learning techniques can be useful in identifying potential exoplanets. To do so, astronomical data gathered by space telescopes such as Kepler and BRITE have been utilized. The method employed to detect exoplanets is Transit Photometry along with Convolutional Neural Network. The study highlights the limitations of small training datasets and suggests the use of data augmentation techniques to increase the size of the training dataset, and the transfer learning approach to improve the performance of the classification models. The research offers valuable insights into the nature and diversity of exoplanets and may open avenues for future discoveries. With a performance accuracy of 96.67%, the proposed approach showcases merit and hence can prove to be a harbinger in exploring planetary habitability in the colossal space.","PeriodicalId":484753,"journal":{"name":"American journal of advanced computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718528","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

Use of "Intelligent Control‟ and "Optimization‟ in Micro-grid System “智能控制”与“优化”在微电网系统中的应用

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21025

Soumyendu Bhattacharjee, Madhabi Ganguly, Saptadipa Das, Devmalya Banerjee, Jinia Datta

引用次数: 0

Artificial Intelligence in Smoking Residue Detection: Bridging the Gap Between Medical Diagnostics and Predictive Analysis 烟灰检测中的人工智能:弥合医疗诊断和预测分析之间的差距

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21013

A. Maiti, A. Roy, C. Dutta, D. Saha

{"title":"Artificial Intelligence in Smoking Residue Detection: Bridging the Gap Between Medical Diagnostics and Predictive Analysis","authors":"A. Maiti, A. Roy, C. Dutta, D. Saha","doi":"10.15864/ajac.21013","DOIUrl":"https://doi.org/10.15864/ajac.21013","url":null,"abstract":"The objective of this work was to create a model that could identify smoking traces in the body and forecast future smoking propensity using a variety of healthrelated variables. Effective detection and monitoring of smoking residues in people is essential for identifying smoking behaviors and evaluating health concerns. The researchers used a cutting-edge strategy that combined medical diagnostics with artificial intelligence (AI) to enable advanced detection of smoking residues in order to overcome this barrier. The suggested methodology makes use of medical diagnostic tools, including an individual's lipid profile and dental test, to record and examine physiological and chemical indications connected to smoking. The vast data generated by modern medical diagnostic methods are meticulously analyzed and comprehended by AI-based systems to get improved accuracy and effectiveness of detecting smoking residue. Voluminous data sets serve as a crucial training ground for machine learning models, enabling them to discern patterns and accurately classify individuals based on their smoking habits. The study demonstrated a 99% prediction performance, making it a valuable tool for healthcare institutions to better understand and predict the likelihood of hospital admissions related to smoking. In the future, the study aims to determine the concentration of nicotine or cotinine and detect heart disease and lung conditions.","PeriodicalId":484753,"journal":{"name":"American journal of advanced computing","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718533","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

Automated Transmission Line Fault Detection using Distance Locator 使用距离定位器自动检测传输线故障

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21014

Subhamoy Dey, Barnali Kundu, Souvik Pal, Aniruddha Sengupta, Manoshi Biswas

引用次数: 0

Human Centric Computing Applications for Laptop Price Prediction 以人为本的电脑价格预测应用

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21021

Mehboob Zahedi, Danish Jamal, Abhishek Das

{"title":"Human Centric Computing Applications for Laptop Price Prediction","authors":"Mehboob Zahedi, Danish Jamal, Abhishek Das","doi":"10.15864/ajac.21021","DOIUrl":"https://doi.org/10.15864/ajac.21021","url":null,"abstract":"With the rapid enhancement of modern technology, we are more engaged with online shopping due to its high comfort, ease to use, safety etc. So we find a problem for laptop product evaluation in the online as well as offline market. The demand for laptops were rapidly increased after the lockdown in India. In the June quarter of 2021, 4.1 million units were shipped and which is the highest shipment in five years. In laptops, the price is acquired from its RAM, ROM, CPU, GPU, Touch screen, model, trends etc. Sometimes it is very much difficult for the customer as well as the retailer to fix a price with the certain characteristics of laptops so that both can evaluate the price and be satisfied with it. So we are going to develop a model for predicting the laptop price as per its properties. Because of any casual customer, this model will help in selecting and deciding on a laptop whether to buy or not, and also will reduce the time and effort that anyone will have to spend manually researching the market price. This paper will focus on Human-centric computing applications for laptop price prediction because it can be analyzed by those well- structured data that itself enhanced machine learning techniques, easily representable as a set of qualified parameters etc. So, we will develop an attribute-based prediction model for laptops using Regression machine learning algorithm.","PeriodicalId":484753,"journal":{"name":"American journal of advanced computing","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718535","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

Contactless Attendance System Using Raspberry Pi4 使用Raspberry Pi4的非接触式考勤系统

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21017

Madhurima Roy, Rajdeepa Das, Rajatsubhra Pal, Kaushik Roy, Joyati Chattopadhyay

{"title":"Contactless Attendance System Using Raspberry Pi4","authors":"Madhurima Roy, Rajdeepa Das, Rajatsubhra Pal, Kaushik Roy, Joyati Chattopadhyay","doi":"10.15864/ajac.21017","DOIUrl":"https://doi.org/10.15864/ajac.21017","url":null,"abstract":"Traditional attendance-taking techniques have sev- eral flaws. To address these challenges, most institutions have adopted a contemporary approach and embraced technology for greater accuracy, such as RFID and biometric systems. However, these systems have limitations of their own. For example, RFID identification may be lost or misused, resulting in false identifica- tion, and biometrics can be time-consuming, which is a concern since attendance is typically collected during peak hours. Due to these difficulties, both of these strategies are inefficient. Our project aims to create a contactless attendance system that uses deep learning-based facial recognition. This system will allow various businesses to save time and costs while improving security. Our project is an all-in-one package that includes both hardware and software and can be used without the need for additional devices. This makes our proposed system both independent and user-friendly. The proposed hardware system consists of a Raspberry Pi 4, a camera for facial identification, a keyboard for ease of access, and a touch-enabled screen. We use OpenCV's face detection and the deep learning-based dlib package, which allows our solution to be efficient on a low-power computing device like the Raspberry Pi, making it deployable anywhere.","PeriodicalId":484753,"journal":{"name":"American journal of advanced computing","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718538","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

AI DBMS in modern-day applications AI DBMS在现代应用

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21012

Barsha Rani Shaw, Biprojit Halder, Soham Sen, Sumi Basak, Sumanta Bhattacharya

引用次数: 0

Design and Development of Automated Self Recoverable Drone 自动自恢复无人机的设计与开发

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21018

Simanchal Pattanayak, Sayantani Das, Sudipta Das, Nilay Saha, Soumik Podder

引用次数: 0

Comparative Analysis of Deep Learning Models for the Detection of Epileptic Seizure 深度学习模型在癫痫发作检测中的比较分析

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21016

Belal Arshad, Atin Mukherjee

{"title":"Comparative Analysis of Deep Learning Models for the Detection of Epileptic Seizure","authors":"Belal Arshad, Atin Mukherjee","doi":"10.15864/ajac.21016","DOIUrl":"https://doi.org/10.15864/ajac.21016","url":null,"abstract":"Electroencephalogram (EEG) is used to detect epilepsy, a common neurological disorder. Neurologists visually examine EEG results to make the diagnosis. Researchers have suggested automated technologies to diagnose the seizure because traditional method are lengthy and there is a dearth of professionals everywhere. The common symptoms of seizures, which are characterized by aberrant brain activity brought on by an epileptic disease, include bewilderment, loss of awareness, and strange behaviour. Sometimes it becomes very difficult to identify the seizure in persons. So, for determining seizures there are many deep learning models have been designed. Among those, three models have been chosen and compared in this paper. These three models are, Convolutional neural network-long short-term memory (CNN-LSTM), convolutional neural network-recurrent neural network (CNN-RNN), and convolutional neural network-gated recurrent unit (CNN-GRU) whose comparison study have been discussed in this paper by using three different types of optimizers, namely Rmsprop, Adam, and Nadam. After that the result of deep learning models have been compared with some previous machine learning work for the detection of epileptic seizure. Mainly three parameters such as accuracy, sensitivity and specificityof the models are found and compared to predict which model as well as which optimizer among Rmsprop, Adam and Nadam is best. For efficient removal of the features from an EEG sequence data, one dimensional convolutional neural network (CNN) is created. For further extraction of temporal characteristics, the features extracted are processed by the CNN-LSTM model's LSTM layers, CNN-RNN model's RNN layers, and CNN-GRU model's GRU layers. The last epileptic seizure recognition step involves feeding the output characteristics into a number of fully connected layers. I","PeriodicalId":484753,"journal":{"name":"American journal of advanced computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718536","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

“Guide-To-Go”: A Cutting-Edge Approach to Building a Real-Time Travel Guide Application “Guide- to - go”:构建实时旅游指南应用程序的前沿方法

American journal of advanced computing Pub Date : 2023-04-03 DOI: 10.15864/ajac.21019

Dibakar Das, Monalisa Halder, Riddhiman Mukhopadhyay, Shubhadip Atta, Jeet Dutta, Chandan Kumar Raul, Sayanti Das, Soumadeep Dutta

引用次数: 0