2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)最新文献

{"title":"Implementation and realization of all-optical multiplexer and encoder using SOA","authors":"Sidharth Semwal, Sanmukh Kaur, Anil Shukla","doi":"10.1109/ICICICT54557.2022.9917696","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917696","url":null,"abstract":"In this paper, we propose an all-optical 4:1 multiplexer using AND, OR and NOT logic gates based on semiconductor optical amplifier (SOA). A 4:2 all-optical encoder has also been realized employing two OR gates based on SOA. The proposed devices work on the principle of cross phase modulation, which occurs as a result of interaction of photonic wave fields in an optical medium, causing phase modulation as a result of changes in refractive index caused by one optical field.Operation of proposed multiplexer and encoder has been achieved at 100 Gbps thus verifying successful operation of the devices.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131451214","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":"Comparison of Deep Learning-Based Methods for Electrical Load Forecasting","authors":"Angel T. S., Abhishek Praveen, Hashim Mohideen S., M. L. Narasimhan, R. V, K. P.","doi":"10.1109/ICICICT54557.2022.9917663","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917663","url":null,"abstract":"The introduction of Artificial Intelligence based methods for forecasting the load in power systems has shown remarkable results in terms of accuracy. A proper forecast of the load ahead can be beneficial in terms of planning, scheduling, and regulating the usage of power to minimize its cost of generation, wastage and to improve the system reliability. Numerous AI-based methods have been used for the purpose of forecasting the load. In this paper, three deep learning algorithms namely Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) and Vanilla Recurrent Neural Network (RNN) were used for load prediction. The dataset has been taken from PJM (East Region). The accuracy of models was examined and compared on the basis of values obtained for Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The experimental results show that the LSTM is reliable and accurate than other two models for the forecasting of electrical load in a power system.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132379872","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":"Multinomial Logistic Regression Classification Model for Arrhythmia Detection","authors":"Prajitha. C, S. P, B. S","doi":"10.1109/ICICICT54557.2022.9917575","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917575","url":null,"abstract":"In the present medical era, Electrocardiography has been used to record heart activities for the basic visualization of various cardiac diseases. ECG signal properties analysis for effective arrhythmia classification has been considered a challenging issue due to the interruption of various sorts of noises that includes baseline wander, power line interference, and motion artifact noise. This challenge has been addressed in this research through the Multinomial logistic Regression (MLR) classification model. This mathematical model has been structured for the effective removal of various noises and to improve the classification ratio for effective arrhythmia detection from ECG signals. In MLR, Fractional Wavelet Transform is used for preprocessing of ECG signal for removing the noise and to determine the QRS interval from the ECG signal. From the Pre-processed signal Stacked Autoencoder (SAE) is used to validate the dimensionality of the retrieved features for effective prediction and classification of multiple forms of arrhythmias. Based on the extracted features of the ECG data MLR classification model obtains a maximum classification ratio for accurate arrhythmia identification. The experimental findings show an improved classification ratio of 98.95% with reduced noise factors, Signal to Noise Ratio (SNR) of 35.7dB when compared to conventional algorithms.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133402578","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":"Deep Learning Techniques to Detect Learning Disabilities Among children using Handwriting","authors":"V. Vilasini, B. Banu Rekha, V. Sandeep, Vishnu Charan Venkatesh","doi":"10.1109/ICICICT54557.2022.9917890","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917890","url":null,"abstract":"In today’s world, we come across children facing certain disabilities which pose as obstacles and hinder their academic growth. Some of these disabilities are explicitly visible to the common eye, whereas some are hard to find and need extra attention. One such condition is Motor Dysgraphia which challenges an individual’s ability to write. The common practice that is followed to identify such a condition among children is quite expensive and creates a mental strain on them. There are many intelligent computational methods that have been proposed with bearing a wide range of performances, however they are not quite standardized for assessment. Fortunately the advancements in Deep Learning techniques have been proven beneficial in automating this identification task. In this study, Learning Disability Detection system is built using Deep Learning techniques. The project’s application is mainly focused on the pre-school and primary school children. This model analyses the child’s handwriting and classifies whether the child is subjected to such a disorder or not. Deep Learning models - Convolutional Neural Networks (CNN) and Vision Transformers are adapted and their Disability Detection performances are analyzed and compared.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131749480","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":"Paddy Leaf Disease Detection using Deep Learning Methods","authors":"Indukuri Gowtham Kishore, Kakaraparthi Phanindra Kumar, C. VamsiKrishna., Esarapu Dilip Vignesh, Potham Raghavendra Reddy, Aswathy K. Nair","doi":"10.1109/ICICICT54557.2022.9917886","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917886","url":null,"abstract":"Computerised automated diagnosis of crops disease enables early detection and ensures the quality of crop. Technology advancements in these fields will reduce the loss and increase the overall productivity. Our research work motivated to build a deep learning classification model for paddy leaf disease detection. The model frame work consists of several pre-processing techniques such as denoising, data filtering, and selection of optimizer that best fits the model. Finally, a comparative study of the proposed model’s performance and efficiency was done with different deep learning models. Based on the analysis and observation, it was observed that the proposed model has given promising results for effective leaf disease detection.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132117451","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":"Computational Approaches to Detect Human in Multifaceted Environmental Conditions Using Computer Vision and Machine Intelligence – A review","authors":"Gaytri Bakshi, A. Aggarwal","doi":"10.1109/ICICICT54557.2022.9917742","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917742","url":null,"abstract":"Human detection is a very significant research problem in different arenas depending upon the adopted application. The main motivation that dominated the area are related to security and natural disasters. Considering the varying wide geographical area of the earth comprising of different countries with mountains, plains, deserts, plateaus and strong river system supporting the human civilization, there are problems dealing with the security system as well as ruining the normal day-to-day life with upcoming natural calamities. To tackle both the major issues with the growing technology and to serve the humanity, specific scientific, automatic, mechanically robust systems have been developed which intelligently provide solutions by detecting the upcoming problems. This paper depicts a study on various aspects on environmental and climatic conditions that could affect human detection and researchers’ endeavors to achieve results with utmost accuracy.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130305503","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 Hybrid Model for Skin Disease Classification using Transfer Learning","authors":"S. Kusuma, G. Vasundharadevi, D. M. Abhinay Kanth","doi":"10.1109/ICICICT54557.2022.9917705","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917705","url":null,"abstract":"Worldwide, around two people die each hour due to skin cancer. The disease is normally originated by expose to sunrays. Early detection is very important to prevent it from spreading. The traditional method of detecting skin cancer is through a procedure known as Biopsy. This is an invasive and time-consuming procedure that involves removing the skin cells. With the advancement of imaging techniques, early detection of skin cancer can be made possible. A study has been conducted to develop two deep learning architectures that can automatically detect skin cancer using 3700 clinical images. One of the architectures is based on the AlexNet framework, which is a transfer learning algorithm. The other one uses a hybrid structure that combines the long short term memory and the temporal properties of the images. The first architecture, which is based on the AlexNet framework, has an accuracy of 99.25%. However, the second hybrid structure, which is a combination of the long-term memory and the temporal properties, has an accuracy of 99.75%. The results of the study contribute to the field of the deep structural model.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131602932","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":"Teach less and Practice more: Flipped Learning for Nursing Education","authors":"S. Chaturvedi, C. Chaturvedi","doi":"10.1109/ICICICT54557.2022.9917815","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917815","url":null,"abstract":"It is essential that students and teachers work together to acquire even the most basic nursing care skills to the full extent of the Nursing Process. The nursing procedure is not something that can be learnt in a single day by any student. There were various difficulties for both learners and mentors during the COVID-19 outbreak when e-learning was used as a mode of instruction. This project was designed to efficiently address learning needs during a pandemic. In this study, baccalaureate nursing students were taught the nursing process using a flipped classroom methodology with the goal of developing knowledge and skill in the nursing process.To teach the nursing process during the covid-19 pandemic; A Flipped learning program was planned with 4 online modules to teach the nursing process. To see the effectiveness one group posttest only design was adopted for 36 students selected by consecutive sampling. The whole program was planned with an integrated pedagogy in pre-classroom, during the classroom, and post-classroom activities to attain the objective of comprehensive knowledge & skills acquisition for delivering the nursing process. Teaching for theory was done virtually with prerecorded Video-lecture, PowerPoint Presentation, Pdf. asynchronous & synchronous discussion and demonstration of procedures were done face to face in the skill lab. The final assessment was performed by a self-structured knowledge questionnaire and a practicum exa mination through OSCE in the laboratory.The result shows that students scored mean knowledge of 15.2 out of 20 with a standard deviation of 2.6 and a practice score of 9.42 out of 12 with a standard deviation of 3.1.If the pedagogy is engaging, from simple to complex, and the whole process is accompanied by self-learning material on computers, the nursing process can be made easier. Students' critical thinking and synthesizing abilities have been boosted by classroom activities. This course has provided students with the opportunity to self-learning with computers so that they can use that knowledge to improve nursing services in the future.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122402348","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":"The Information Technology and Employee Performance in the Indian Telecom Industry","authors":"T. Joseph, R. Radhika","doi":"10.1109/ICICICT54557.2022.9917852","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917852","url":null,"abstract":"Abstract-The study aims to analyze the impact of information technology on the performance of the employees working in the telecom sector. This descriptive study used regression for identifying the relationships of the variables. Data was collected from 305 respondents through the online questionnaire. After reviewing the existing literature, the data is analyzed using the t-test, Fisher's test, and the coefficient of determination. It was found through the results of regression analysis, that information technology has a significant positive impact on the performance of the employees working in the telecom sector. Therefore, the hypothesis is been accepted for the study. It is also proven by administering simulation testing. In addition to this, this study also throws light on the benefits and the contributions of technology to improving employee performance.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122617472","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":"Consumer demand profile management through Demand side load management: a review","authors":"Shailendra Baraniya, S. Zahid Nabi Dar","doi":"10.1109/ICICICT54557.2022.9917905","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917905","url":null,"abstract":"Demand side management is an intervention by the consumer or utility, to effect changes in demand profile for the grid or a cluster of consumers. The load profile management is usually done to bring down the peak demand for the period (usually a daily peak demand). This is done through voluntary participation of consumer. Demand side management is an important tool available to consumers to take advantage of differential tariffs in force now-a-days. The utilities are equally or more benefited by the DSM implementations for its advantages including improvement in overall efficiency, reliability and reducing denial of service. This work presents the basics concepts of DSM and discusses select DSM implementations, reported by various authors. This work discusses various architectures useful in DSM implementations.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074425","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}