{"title":"Performance Evaluation and Comparison using Deep Learning Techniques in Sentiment Analysis","authors":"P. A.","doi":"10.36548/jscp.2021.2.006","DOIUrl":"https://doi.org/10.36548/jscp.2021.2.006","url":null,"abstract":"One of the most common applications of deep learning algorithms is sentiment analysis. This study delivers a better performing and efficient automated feature extraction technique when compared to previous approaches. Traditional methodologies like surface approach will use the complicated manual feature extraction process, which forms the fundamental aspect of feature driven advancements. These methodologies serve as a strong baseline to determine the predictability of the features, and it will also serve as the perfect platform for integrating the deep learning techniques. The proposed research work has introduced a deep learning technique, which can be incorporated with feature-extraction. Moreover, this research work includes three crucial parts. The first step is the development of sentiment classifiers with deep learning, which can be used as the baseline for comparing the performance. This is followed by the use of ensemble techniques and information merger to obtain the final set of sources. As the third step, a combination of ensembles is introduced to categorize various models along with the proposed model. Finally experimental analysis is carried out and the performance is recorded to determine the best model with respect to the deep learning baseline.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72986869","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":"Comparative analysis of the efficacy of selected gametocide agents in sorghum (Sorghum bicolor [L.] Moench)","authors":"M. Yahaya, H. Shimelis, M. Laing, I. Mathew","doi":"10.21475/ajcs.21.15.06.p2801","DOIUrl":"https://doi.org/10.21475/ajcs.21.15.06.p2801","url":null,"abstract":"A new generation of chemical hybridization agents (CHAs) or gametocides has shown potential to induce male sterility in predominantly self-fertilizing crops, including sorghum (Sorghum bicolor [L.] Moench). There is a lack of information on the relative efficacy of the various available CHAs for large-scale application in plant breeding programs. Therefore, the objective of the present study was to compare the relative effectiveness of three selected CHAs to induce male sterility in sorghum under a controlled environment for hybridization. Foliar applications of three CHAs and a control (ethrel, trifluoromethanesulfonamide [TFMSA], ethyl 4-fluorooxanilate [E4FO] and distilled water [control]) were tested using three grain sorghum genotypes (ICS-1, ICS-2 and ICS-3) in two seasons. The 24 treatment combinations consisting of 4 levels of CHAs, 3 sorghum varieties and two seasons were laid out using a randomized complete block design with three replications. Data on pollen sterility, pollen diameter, plant height, and panicle height were collected and analyzed. Results showed that the CHAs had significant (p<0.05) differences for efficacy of inducing male sterility in sorghum. Ethrel at a dose of 1 gl-1 induced the highest pollen sterility (98% in both seasons) but was highly phytotoxic with at least 60% mortality in the test population in both seasons, making it unsuitable for practical application. TFMSA (2 mg per plant) and E4FO (1 gl-1) d induced 93% male sterility with minimal phytotoxic effects (20 to 30%). Application of either TFMSA at 2mg per plant after flag leaf emergence or 1gl-1 of E4FO at panicle initiation can be used to successfully induce male sterility in sorghum under greenhouse conditions","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":"596 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77626998","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":"Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network","authors":"Akey Sungheetha, Rajesh Sharma R","doi":"10.36548/jtcsst.2021.2.002","DOIUrl":"https://doi.org/10.36548/jtcsst.2021.2.002","url":null,"abstract":"Early identification of diabetics using retinopathy images is still a difficult challenge. Many illness diagnosis techniques are accomplished by using the blood vessels present in fundus images. Many conventional methods fail to detect Hard Executes (HE) present in retinopathy images, which are used to determine the severity of diabetes disease. To overcome this challenge, the proposed research work extracts the features by incorporating deep networks through convolution neural networks (CNN). The micro aneurysm may be seen in the early stages of the transformation from normal to sick condition on the images for mild DR. The level of severity of the diabetes condition may be classified by using the confusion matrix detection results. The early detection of the diabetic condition has been achieved through the HE spotted in the blood vessel of an eye by using the proposed CNN framework. The proposed framework is also used to detect a person’s diabetic condition. This article consisting of proof for the accuracy of the proposed framework is higher than other traditional detection algorithms.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82232004","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}
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