2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)最新文献

{"title":"Design of a Robotic Arm equipped Fully Automated Burger Assembly Machine","authors":"R. K. Phanden, Tushar Chaudhary, P. Srivastava, S. Sharma, Pardeep Gahlot, Kapil Kumar Goyal","doi":"10.1109/ICAECT54875.2022.9807651","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807651","url":null,"abstract":"Automation plays a vital role in food preparation since it reduces human intervention, leading to maintaining hygiene. In India, a patty on the bun (called burger) is a popular fast food. However, most of the restaurants and café are lacking automated burger assembly machines. In this paper, the design of a robotic arm-equipped fully automatic burger assembly machine has been proposed. Initially, the automated burger assembly machine is fabricated, and later the robotic arm has been introduced to make the machine fully automatic. The design and analysis are performed using SolidWorks®. The performance is evaluated through the simulation in ProModal® software. The performance of both scenarios, (i) partially automated machine and (ii) fully automated machine, has been evaluated and verified. Results reveal that the total output of burgers (i.e., 479 units) is not changed after robotic arm deployment. The proposed design can be improved by making IoT enabled ordering and servicing system to achieve the next level of automation.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128911628","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}

{"title":"Movie Recommendation Based on Mood Detection using Deep Learning Approach","authors":"Tahasin Elias, Umma Saima Rahman, Kazi Afrime Ahamed","doi":"10.1109/ICAECT54875.2022.9807654","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807654","url":null,"abstract":"With each passing day, new technologies are introduced to humans, bringing them closer to computers and forming a strong bond between them. Image processing is a boon to the world in today's technological age. In the realm of image processing, many research fields have emerged, such as mood detection, object detection, signature detection, and so on, with mood detection emerging as the most popular research area today. The most delicate way to interpret a human's mind, as well as a human's demand, is through facial expression. A human's desire, such as watching a movie, may be predicted using this facial expression, which saves consumers time and effort in looking through a movie list. This paper represents an approach of movie recommendation based on mood detection that employs a couple of neural networks such as CNN, VGGNet, Inception, MobileNet, and DenseNet. These neural networks can recognize facial expressions and can also propose movies based on this information. At last, we compare the results of our datasets to the results of the collected datasets.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128942312","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}

{"title":"Automated MRI Brain Tumour Segmentation and Classification Based on Deep Learning Techniques","authors":"K. Srilatha, P. Chitra, M. Sumathi, Mary Sajin Sanju. I, F. V. Jayasudha","doi":"10.1109/ICAECT54875.2022.9807965","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807965","url":null,"abstract":"A brain tumour is a significant death problem among other cancer types because the brain is a susceptible, complicated, and significant part of the human. The precise and appropriate examination can control the lifespan of an individual to a remarkable period. The image-segmentation of MRI (magnetic resonance images) is significant for envisioning and analyzing irregular tissues, notably during a medical examination. Intricacy and modifications of the tumour formation intensify difficulties in computerized brain tumour detection and segmentation in MRIs. This proposed system performs an automated brain tumour segmentation process in the MRI brain image accompanied by classification. Since, in this method, an effective brain tumour detection and classification scheme is intended using fusing GLCM features and CNN. The proposed method consists of four steps: pre-processing, image segmentation, extraction of features, and optimization and classification. First, noise elimination is done as the pre-processing step at the brain MR images. Following the classification method, irregular brain MR images are provided to the segmentation part to detect tumours and segments using the fuzzy c means (FCM) technique. Following that, GLCM and Ant colony optimization (ACO) which features are obtained from these noiseless MRI images of the brain. A tremendous numeral of features is decreased founded on Ant colony optimization (ACO). Finally, chosen features of brain images are provided to the CNN classifier to categorise MRI brain images as abnormal or normal. The proposed method performance is examined in various metrics, and testing outcomes are comparable to present systems.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122354532","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}

{"title":"Music Note Series Precipitation using Two Stacked Deep Long Short Term Memory Model","authors":"Carmel Mary Belinda M J, M. Shyamala Devi, J. Pandian, R. Aruna, S. Ravikumar, K. A. Kumar","doi":"10.1109/ICAECT54875.2022.9807884","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807884","url":null,"abstract":"People need to get relieve from their stress and thoughts by engaging themselves with entertainment. Music plays a vital role in changing the people environment to overcome their personal problems. As technology is involved in all the fields, deep learning is extensively contributing its performance in the music industry towards generation of music note sequence. Music confirms to be a tough dynamic than image data as it is temporal with hierarchical structure and cross-temporal dependencies. As music is composed of multiple instruments by being interdependent and being evolved over time, it remains a challenging issue for the researchers to work. Since music is distributed into chords, arpeggios, melodies and each time-step generating multiple outputs, the generation of music note sequences through deep learning network extend higher issue for the researchers in programming. By interpreting the above scenario, a new two-stacked Deep Long Short Term Memory (TSD-LSTM) model is designed for music note sequence generation using audio framework of piano with 16000 frame length. The dataset is extracted from Maestro database repository. The audio framework data is converted into MIDI format using pretty_midi package. The MIDI digital data is parsed with 30513 number of notes resulting with sequence length of 25 frames and vocabulary size of 128 pitch length. The total digital data is ended up with 1282 MIDI information frames. The parsed audio digital data is splitted into training data with 967 MIDI data, validation data with 137 MIDI data and testing data with 178 MIDI data. Model fitting is done with TSD-LSTM. The proposed model design is refined with parameter optimization. The project is implemented with Python under NVidia Tesla V100 GPU server with 215938 trainable parameters, training epochs of 300, batch size of 64 along with training rate of 0.001. The music note sequence generation is done with TSD-LSTM and fitted with single stack LSTM, 3 stack LSTM and 4 stack LSTM and the performance is analyzed and compared with metrics like train loss, duration loss, pitch loss and step loss. Implementation results shows that TSD- LSTM model have minimum value for test loss of 0.0567, duration loss of 0.0086, pitch loss of 0.8150 and step loss of 0.0073 compared to other models.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130689250","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}

{"title":"Loads Instability Study of the Gas Turbine Blades for Al- Khums Power Station","authors":"Qays Faraj Alshaybani, Nasar Aldian Ambark Shashoa, A. Abougarair, Elhassen Ali Omer","doi":"10.1109/ICAECT54875.2022.9807866","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807866","url":null,"abstract":"Al-khums gas power station is considered one of the main power stations for electricity generation in Libya. It was built in 1982, and it contributed to a large part of the electricity production in the Libyan electrical system. However, the operating system of this station has various technical problems. One of these problems is the turbine blades. Especially, hair cracks that occur in the first rows of these blades. Erosion occurs at the tip and edge of the blade. In addition, the color of the protective layer is changed and the cooling vents are crashed. One of the most important factors that causing these problems is the loads instability of the plant fourth unit, that leads to the rise and fall of the hot gases temperature that heading to the turbine. In addition to, the fuel quality and weather factors such as temperature and humidity have a major impact on the efficiency of the unit. In this paper, the possibility of installing a two-stage intercooler for the compressor at the fourth unit of the station is presented and its impact on the performance and operating conditions of the station is studied. The results show that the temperature leaving the combustion chamber increases when the temperature leaving the compressor decrease and the thermal efficiency increases after installing the intercooler.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132138667","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}

{"title":"A Multi Band Modified Circular Ring Graphene Patch Antenna For THz Applications","authors":"Smriti Jaiswal, Sakshi Maurya, R. Kumari","doi":"10.1109/ICAECT54875.2022.9807995","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807995","url":null,"abstract":"In this article, the modified circular ring antenna is designed for multiband in THz applications. The graphene material is utilized to design a radiating element which is implanted on silicon substrate having the dimensions 80×80×10µm3. To achieve multiband, outer ring, square ring, and inner ring are connected in radiating element which makes simple design of the single element and easy in fabrication. The proposed antenna operates at 1.54THz, 2.33THz, 2.93THz, and 3.49THz frequency bands. The antenna has simulated S11<-10dB bandwidth of about 19 %, 15 %, THz, 9 %, and 14 %, THz respectively. The good impedance matching, stable radiation pattern, and average gain of 4.5dBi are analyzed in operating bands which makes antenna suitable for cancer detection, biotin detection, and security applications.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123216730","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}

{"title":"An energy-efficient Long HopHigh Priority Algorithm for IoT resource management","authors":"G. Deepa, R. Padma Priya, S. Priyadharshini, V. Sabarinath","doi":"10.1109/ICAECT54875.2022.9807937","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807937","url":null,"abstract":"In this modern era, the Internet of Things (IoT) is one of the hottest technologies which is growing day by day. There are many types of research still underway. Many aspects are yet to be improved for real-time applications. The main area focused in this work is the networking part of the IoT. Therefore there are chances for lots of problems during communication. One main problem is congestion. It may even break the network. So scheduling is done to avoid such situations. The proposed work deals with message scheduling in an IoT environment where the whole environment is divided into several subgroups and each group is headed with a master node called broker node which is responsible for sending the messages. Behind this broker node, a scheduling algorithm runs that decides which message should be sent first and when it should be sent. Many algorithms are existing such as SPTF, LH, FCFS, etc. To enhance the performance of the network a new algorithm named the Long Hop High Priority (LHHP) algorithm is proposed. In this algorithm, emergency messages are given high priority and long distances are taken into consideration, which improves the response time and turn-around time of the messages in an energy-efficient way.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122541646","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}

{"title":"On-board Antenna Tracking System for HTS Satellites: ISRO’s First In-Orbit Success on GSAT-19","authors":"P. Gupta, Santosh Lacheta, N. Desai","doi":"10.1109/ICAECT54875.2022.9807898","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807898","url":null,"abstract":"This paper presents ISRO’s and India’s first successful development and validation of a space borne Antenna Tracking System technology on its experimental HTS satellite GSAT-19. Onboard tracking system demonstration is an enabling technology for a High Throughput Satellite (HTS) program as it is essential to maintain beam coverage of high-gain sharp multiple beam antennas locked to specific coverage zones on the ground to ensure edge-of-coverage gains and variations in spite of spacecraft roll & pitch stability residues. The tracking system senses such fine angular offsets and generates proportional DC error voltages that are used to apply the required correction to the antenna axes through appropriate drive motors. The system has been designed & tested for the tracking accuracy better than 0.01°. The technology has been successfully developed and proven for the first time on an ISRO satellite, paving the way for full-fledged follow-on HTS satellites (GSAT-11, GSAT-20 etc).","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"499 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116607358","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}

{"title":"A Performance Study on Deep Learning Covid-19 Prediction through Chest X-Ray Image with ResNet50 Model","authors":"Dhirendra Kumar, Pulkit Sharma, Anupama Anupama, Parth Sharma","doi":"10.1109/ICAECT54875.2022.9807920","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807920","url":null,"abstract":"The COVID-19 epidemic has claimed many lives throughout the world and constitutes an unprecedented public health concern. The key challenge in early detection of the corona virus is early detection. And the main obstacle was the similarity of COVID-19 symptoms to flu symptoms. With the goal of saving human lives and stemming the spread of a worldwide pandemic, an accurate and speedy analysis of COVID-19-induced pneumonia has now taken centre stage. Responding this urgent concern and to reduce the burden as well as chances of faulty manual diagnosis, several deep learning approaches are developed to conduct early diagnosis. Based on the availability of reliable patient's records, an accepted technique is pre-trained deep learning prediction approach through patient's chest X-Rays. Convenience of this approach led development of a number of novel deep learning-based lung screening technologies. However, little emphasis is placed on ensuring the quality of their output. Pre-trained deep learning systems will be used in this project to evaluate their ability to recognise and diagnose disorders. To categorise COVID and normal pictures, a neural network-based ResNet50 architecture is presented. The implementation is based on the normal, COVID, and lung opacity datasets. For data pre-processing, ImageDataGenerator is used, which rescales, flips, and modifies the range to meet the model. To categorise the x- ray images, the suggested method ResNet50 architecture is used. Performance matrices like precision, accuracy, recall, as well as F1-score are examined to verify the algorithm's usefulness. The suggested technique has an accuracy of 80%, indicating that the proposed model is quite good in classifying COVID and normal x-ray pictures. This research will have a significant influence on real-time since it will accurately diagnose COVID in less time, perhaps lowering the mortality rate.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123773830","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}

{"title":"Food Review Analysis and Sentiment Prediction using Machine Learning Models","authors":"Dhruv Gupta, Ausho Roup, Diksha Gupta, Avinash Ratre","doi":"10.1109/ICAECT54875.2022.9807907","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807907","url":null,"abstract":"In this era of the digital world, text, messages, comments, numbers and videos have become an essential source of information. The trend of people trading through e-commerce giants like Amazon and Flipkart is proliferating. It’s necessary to have a model or tool that helps retrieve helpful information from the customers’ online reviews quickly that can also help product manufacturers have a better idea of their product. This paper targets the food industry, and a model is proposed that analyzes the customer reviews based on NLP techniques- TF-IDF Vectorizer and Count Vectorizer. Based on these analyses, customer sentiments are predicted using different machine learning classification algorithms like Logistic regression, Dummy classifier and Random forest classifier.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121488582","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}