2022 IEEE International Conference on Data Science and Information System (ICDSIS)最新文献

{"title":"ICDSIS 2022 Cover Page","authors":"","doi":"10.1109/icdsis55133.2022.9916000","DOIUrl":"https://doi.org/10.1109/icdsis55133.2022.9916000","url":null,"abstract":"","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129236046","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":"Implementation of Noise Cancellation using Adaptive Algorithms in GNU Radio","authors":"L. Harshitha, Kumari N D Sindhu, C. Rekha, S. Ramya, B. T. V. Murthy","doi":"10.1109/ICDSIS55133.2022.9915931","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915931","url":null,"abstract":"The demand for wireless communication system design is on the rise recent days. When data is transferred through a wireless channel, the noise on the channel has a larger probability of affecting the information. The suggested project attempts to aid in the reduction of noise through the use of an adaptive filter in order to ensure reliable communication. In this way noise cancellation plays an important role in digital communication. GNU Radio is an open framework for building real-time signal processing applications on low-cost single-board computers. C++ and Python language is suitable for backend in GNU radio. MATLAB and python simulation for different algorithms helps in analysing reduced noise and rate of convergence.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131634163","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 Meta-analysis of Machine Learning for the Diagnosis of Covid-19 Disease","authors":"Mona N Gowda, Dalwinder Singh","doi":"10.1109/ICDSIS55133.2022.9915858","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915858","url":null,"abstract":"Coronavirus 2019 has wreaked havoc on people’s lives all across the globe. The number of positive cases is increasing, and the Asian country is now one of the most severely impacted. This article examines machine learning models that are more accurate at predicting covid. Based on the data from China, regression-based, decision tree-based, naive Bayes, and random forest-based models were developed and verified on a sample from India. A data-driven strategy with better precision, such as the one used here, is beneficial for the government and public to respond in a proactive manner. This study reveals that the suggested framework has superior capabilities in detecting COVID-19.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133139102","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":"Optimized Features Based Machine Learning Model for Adult Salary Prediction","authors":"Lokesh Pawar, A. K. Saw, Abhay Tomar, Navneet Kaur","doi":"10.1109/ICDSIS55133.2022.9915981","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915981","url":null,"abstract":"A nation’s economic stability is bolstered and long-term progress is ensured by the idea of universal moral equality. Many governments are putting a lot of effort into addressing this problem and coming up with a workable answer. In order to determine what decisions an adult can make in the future and whether or not he is financially independent and secure, it is predicted in this article whether his wage will be larger than ${$}$50,000 per year or not. Data mining technologies and machine learning algorithms both play a big part in this. This paper focuses on eliminating useless features using various machine learning approaches and algorithms and there is room for improvement. So, using the Gini Index, prominent features are identified and prioritized which applied on machine learning algorithm boosted the performance upto accuracy of 87.82 percent.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132650841","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":"Analysis and Detection of Glaucoma from Fundus Eye Image by Cup to Disc Ratio by Unsupervised Machine Learning","authors":"J. Surendiran, M. Meena","doi":"10.1109/ICDSIS55133.2022.9915887","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915887","url":null,"abstract":"The cup nerve head, eyecup, neural rim shape, and eye disc ratio are useful in identifying glaucoma early in medical practice. The most important medical sign of glaucoma is the eyecup to eye disc ratio, which is presently measured manually by restricting the bulk screening. The following approaches for automatically determining the eye to disc ratio are proposed in this work. The subcapsular image of the eye disc area is studied in the first section. K is utilized robotically to identify the eyedisc in decluttering, whereas K value is continuously determined using a hill-climbing method. Two approaches, morphological and elliptical fitting, have been used to smooth the segmented shape of the eyecup. The eye to disc ratio of glaucoma patients was computed for 60 ordinary images and 60 subcapsular images. The same set of photographs has been utilized throughout this work, and the eye to disc ratio values given by an ophthalmologist has been used as the golden standard value. Throughout the study, the mistake is computed by comparing the eye to disc ratios to this global standard number. For the morphological fitting and elliptical fitting, the average error of the K-means clustering approach is 4.5 percent and 4.1 percent, respectively. The inaccuracy can be further minimized by taking into account the inter-pixel relationship. Another approach is the method used to achieve the aim is Spatially Weighted Fuzzy C-means Clustering (SWFCM). Fuzzy C-mean clustering was chosen because to the huge error, and the method's mean error for morphological and elliptical fitting is 3.52 and 3.83 percent, respectively. SWFCM Clustering has clustered and segmented the eye disc and eyecup. The SWFCM mean error clustering method yields 3.06 percent and 1.67 percent, respectively, for the morphological and elliptical fitting. Sub capsular pictures were obtained from a famous eye hospital in pondy named Aravinda Eye Hospital for this purpose.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127181162","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":"Optimizing Large Gravitational-Wave Classifier Through a Custom Cross-System Mirrored Strategy Approach","authors":"A. Shaikh, Abhra Ghosh, Atharva Chidambar Joshi, Chaudhary Shivam Akhilesh, S. Savitha","doi":"10.1109/ICDSIS55133.2022.9915926","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915926","url":null,"abstract":"The recent detections of gravitational waves from merging binary black holes have opened the doors for a new era of multi-messenger astrophysics. Sensitive gravitational wave detectors such as the “Laser Interferometer Gravitational-wave Observatory” (LIGO) are able to observe these signals, therefore confirming the general theory of relativity by Einstein. However, detecting faint gravitational waves (GW) signals remains a major challenge, and quickly training a good model with an enormous training data set is an even bigger challenge. To overcome these challenges, we have proposed a system that uses a cross-system mirrored strategy (distributed learning) to train the model in minimal time. To detect the faintest of the signals, we used 2D CNNs where we converted the 1D time-series data to a 2D spectrum using Fourier Transforms. This was done to extract the maximum possible features. By using distributed learning, we were able to concurrently train local models on different devices and got the final local weights. Then we aggregate all these local weights in a single system and get the final solitary global model. By using this technique of training the model, we were not only able to comfortably manage very large datasets (100s of GBs) but we were also able to finish the model training 4.5 times faster than all the prior state-of-the-art models.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115403351","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":"Efficient Weed Classification and Disease MANAGEMENT using Multivariate Principle Component Analysis on Spectral Evolution Using Change Detection Methods","authors":"O.Visali Priya, R. Sudha, A. Vaideghy","doi":"10.1109/ICDSIS55133.2022.9915803","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915803","url":null,"abstract":"Weeds are unwanted plant or crops in the agriculture region which leads to primary pest problem in modern agriculture farming. In order to control area specific weed control on basis of classification and management of disease in farmland, hyperspectral images have been acquired from the satellite images in the remote sensing area. With different observation conditions and sensor characteristics, hyperspectral image classification based on spectral evolution simultaneously extracts the sets of spectral signatures of endmembers and maps the corresponding abundance maps from multiple spectral images. It then utilizes multiple supervised and unsupervised mechanisms for class-specific variations on weed and its diseases. Obviously mapping method degrades on accuracy of the coupling of the spectral evolution simultaneously. In this paper, a novel efficient weed classification and disease management on spectral evolution mapping should be proposed using Multivariate principle component analysis. It is examined as change detection mechanism which explores variation in the class features efficiently as the context of images is basis of bands of weed plant and its associated plant diseases, further it leads to a good tradeoff between wider receptive field and the use of Context is employed towards mapping Agriculture Land cover spectral evolution in the hyperspectral images. Proposed approach is capable of computing the spectral correlation among two images with respect to spectral similarity. Finally, it predicts the large intra class variation of weed accurately on temporal changes of the agriculture surfaces along various climate seasons and fields. Experimental analysis of the proposed mechanism was validated on Landsat 8 dataset to compute overall accuracy of the model on the changes in the weed and its diseases. The results of the work exhibits that proposed model can enhance the classification accuracy and reduces the differences of multi-temporal effects compared with existing state of art approaches.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115743960","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":"Real-Time Face Mask Detection Using Streamlit,TensorFlow, Keras and Open-CV","authors":"R. N. S. V. Yalamarthi, Shareef Shaik, Dalwinder Singh, Manik Rakhra","doi":"10.1109/ICDSIS55133.2022.9915817","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915817","url":null,"abstract":"This paper is based on the protection of our health from coronavirus officially known as COVID-19. Real-time detection of a face mask can help to prevent of the coronavirus, detecting the mask with the help of machine learning and data science algorithms such as Streamlit, MoblieNetV2, OpenCV, etc., are widely used in this ideal methodology. This paper is about the method that provides an accuracy of 99.78% in detecting the mask with live video stream. The method proposes building accurate model and integrating the model with a graphical interface which can improve the experience of the user.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128454397","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":"Jamming Attack Detection Using EWMA in Clustered Wireless Sensor Network","authors":"Santhosh Kumar B.J, Shravya C.S","doi":"10.1109/ICDSIS55133.2022.9915936","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915936","url":null,"abstract":"Wireless Sensor Networks (WSN) are one among the most promising networks, with a lot of applications in healthcare, agriculture, weather forecasting and military etc. Due to their shared medium, wireless networks are susceptible to many security attacks. DoS jamming attacks are one among them that interrupt the normal activities of sensor the nodes in wireless network by sending RF signals to jam the communication channel, either knowingly or unknowingly. This work gives a step-by-step approach using Exponentially Weighted Moving Average (EWMA) algorithm by using the Signal to Noise Ratio (SNR) of the received packet as a metric which determines the presence of a jamming attack in a clustered wireless sensor network based on the measured value. Also, some of the parameters such as delay and throughput are analysed using a graph. Result shows that the approach can detect jamming attacks efficiently and nodes that generate jamming attacks are removed from the network.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117187137","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":"Power Quality Improvement in Grid Connected Solar Energy For Harmonic Analysis","authors":"Anjali Patel, R. Singh","doi":"10.1109/ICDSIS55133.2022.9915883","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915883","url":null,"abstract":"Though the efficiency of electricity production from Grid Connected Solar Energy (GCSE) is lower than conventional resources, but it is the need of time to move towards green form of power. The conventional resources are depleting with rapid pace and moreover they have high carbon emission which leads to global warming. As the remedy to this solar had emerged as an effective solution and as the result of enormous research in the solar technology, its mass deployment is now economical and efficient. The key issues which were the hurdles in its mass deployments namely; power quality improvement, synchronization, power flow monitoring and availability of neutral line for harmonics mitigation has been addressed to a great extent. This work also presents the quality enhancement of large solar plant in grid-connected mode under the condition of linear and non-linear loads. The components used and control strategies are briefly discussed to develop the GCSE system.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125275898","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}