2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)最新文献_第5页

Electronic Waste Mitigation using Photovoltaic Systems 利用光电系统减少电子废物

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596361

R. Ibrahim, V. Shukla, A. Yadav, S. Pillai, Nitin Pandey

{"title":"Electronic Waste Mitigation using Photovoltaic Systems","authors":"R. Ibrahim, V. Shukla, A. Yadav, S. Pillai, Nitin Pandey","doi":"10.1109/icrito51393.2021.9596361","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596361","url":null,"abstract":"Solar energy is an infinite source of energy that, if properly harnessed, would make mankind devoid of using the traditional energy sources, which has been used for a long time. Solar energy has come a long way during last decades years. Advances in technology over the next few years will make solar energy much affordable. By 2030, solar will be the most important source of energy for power generation in many countries around the world. It will also have a positive impact on climate change and the environment. Advancements in technology introduced the production of millions of electronics items per day worldwide. These electronics items on completion of their lifecycle add to the electronic waste or e-waste. E-waste is a serious concern for global waste management agencies worldwide as due to their toxicity, neither can it be used in landfills nor left unattended. Various solutions have been explored by the researchers for the mitigation of e-waste. The use of solar charging in electronic systems has shown lots of promise. This paper discusses the impact of e-waste along with technologies to reduce it.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129048084","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: 1

Pursuit for Authentic News using Machine Learning Models 使用机器学习模型追求真实新闻

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596286

Rahul Pradhan, Pragya Tiwary, P. Agarwal, D. Sharma

{"title":"Pursuit for Authentic News using Machine Learning Models","authors":"Rahul Pradhan, Pragya Tiwary, P. Agarwal, D. Sharma","doi":"10.1109/icrito51393.2021.9596286","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596286","url":null,"abstract":"Fake news spreads like a wildfire and this is a big issue in this era. The Online world contains lots of fake news from various sources and channels by political parties, influential peoples, and bots. People are not good for easily able to distinguish fake news from real one. This will negatively impact people's lives, society, and the world. To maintain stability from the harm done by fake news, we will tackle the topic of fake news detection. we are investigating the effectiveness of machine learning technologies and also introducing a deep learning model for fake news detection with better accuracy. We are using Natural language processing with the above technologies for our purpose. In investigating the effectiveness of machine learning technologies, we had applied various classification algorithms on our data processed by NLP to obtain their accuracy for the problem. Then using new emerging technologies like deep learning, we are proposing solutions with better accuracy. In the machine learning technologies for fake news detection, we have found that AdaBoost classifier, Gradient boosting classifier, and Logistic regression are better in terms of accuracy than other classifiers like decision tree, KNeighbors classifier, Random Forest classifier, and MultinomialNB. But These technologies are more prone to error when a different category of data comes. Deep learning technology used here is the Long short-term memory deep learning model which gave us an accuracy of more than 0.993 and the Bi-directional LSTM model with accuracy near 0.99 taking more time in training than the LSTM model. Through this research, we conclude that machine learning technologies perform worse than deep learning technologies. And proposed LSTM model is better than the Bi-directional LSTM model for fake news detection.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132480764","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

Digital Banking: Leverage to Banking Clients 数字银行:对银行客户的影响

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596556

Sadhana S. Thatte, S. Kulkarni

{"title":"Digital Banking: Leverage to Banking Clients","authors":"Sadhana S. Thatte, S. Kulkarni","doi":"10.1109/icrito51393.2021.9596556","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596556","url":null,"abstract":"Novel technology has formed the new track of payment transactions. Electronic payments are accelerating this drive, and new developments including Big Data, ubiquitous internet access, and cloud computing have made an enormous global impact. Financial institutions have engaged in leveraging technology for enterprise growth, offering multiple choice of payment methods than before. The appearance of tomorrow's banking may be a stark contrast to what is in currently today due to upgrading in technology. Businesses must recognise what banks offer the options that fulfil clients' expectations. Banks realised that leverage mobile and other digital technologies have made their clients' lives much convenient. The technology has opened new options to banking clients but they are facing many challenges. The objective is to explore demographic, beneficial factors and challenges in adopting digital technology. The current empirical study concluded variables as saving time, and need not be present in the banks are the most influencing in the acceptance of new-technology and the variables as friends and families followed by the internet are playing a big role in increasing awareness of M-banking among the clients.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131972800","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

Hybrid Load Scheduling in Content Delivery Network Comprising of Under Populated Clusters 由人口不足集群组成的内容分发网络的混合负载调度

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596177

D. Sarkar, N. Rakesh

{"title":"Hybrid Load Scheduling in Content Delivery Network Comprising of Under Populated Clusters","authors":"D. Sarkar, N. Rakesh","doi":"10.1109/icrito51393.2021.9596177","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596177","url":null,"abstract":"Serving content to the worldwide distributed clients with high availability, high efficiency and minimum delay is the primary challenge for today's virtual world. Content delivery network (CDN) was evolved with the aim to send the data at the client's doorstep with almost zero latency. CDN disseminates the content at various edge servers deployed to the client's close proximity. But locating the positions for deployment and also sharing the load among the servers are two major concerns for CDN. In this paper, unsupervised K-means clustering is considered for selecting the locations for server deployment which also considers the under populated clusters based on a parameter called population threshold. This paper introduces a hybrid load sharing model which is mainly concerned about the traffic coming from the clusters with population lesser than the threshold. The result of the study shows that this hybrid approach enhances the server utilization factors of the surrogates deployed in the network while minimizes the server maintenance and cost overhead.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130918858","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: 1

Early Breast Cancer Diagnosis and Risk Prediction based on Machine Learning 基于机器学习的早期乳腺癌诊断和风险预测

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596193

Aryan Mital, Yogesh, Namra Shamim, Bharath Chandra B, U. Keshwala

{"title":"Early Breast Cancer Diagnosis and Risk Prediction based on Machine Learning","authors":"Aryan Mital, Yogesh, Namra Shamim, Bharath Chandra B, U. Keshwala","doi":"10.1109/icrito51393.2021.9596193","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596193","url":null,"abstract":"Breast cancer is a disease in which life-threatening (malignant) cells in the breast multiply out of hand, making it the second most fatal type of cancer in women widely. Hence, to diminish the mortality rate and increasing the chances of survival, it is crucial to uncover it as early as attainable. This paper focused on comparing the different classifiers which are support vector machine, naïve Bayes, and K-nearest neighbor algorithms using the DDSM dataset. The target of this computer-aided system is to combine these classification techniques with image pre-processing methods so to compare their performance to find out the most satisfactory approach. The crux is to use the advantages of these techniques to obtain maximum optimal performance. For the comparative study, the digital mammogram of the breast is passed to histogram equalization for image pre-processing which enhances the necessary feature while removing noise that is present in the mammogram, the refined mammograph is then passed to wavelet transformation to extract all the important features for the classification.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129241464","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

Email Ingestion Using Robotic Process Automation for Online Travel Agency 在线旅行社使用机器人流程自动化的电子邮件摄取

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596472

Urvashi Sharma, D. Gupta

引用次数: 0

A Semi-Supervised Domain Adaptation Approach for Diagnosing SARS-CoV-2 and its Variants of Concern (VOC) 基于半监督域自适应的SARS-CoV-2及其相关变异诊断方法

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596381

A. Khattar, S.M.K. Ouadri

{"title":"A Semi-Supervised Domain Adaptation Approach for Diagnosing SARS-CoV-2 and its Variants of Concern (VOC)","authors":"A. Khattar, S.M.K. Ouadri","doi":"10.1109/icrito51393.2021.9596381","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596381","url":null,"abstract":"Since December 2019 the world has been facing an unprecedented crisis in handling the outbreak of the biological disaster COVID-19 putting a lot of pressure on the healthcare systems globally. Poor collection of swab samples, delayed testing, or variations that have possibly changed the disease patterns may be the reasons that have led to the increased false-negative results of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests recently which is considered the gold standard to detect Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). At such times the doctors have to depend on X-rays or CT scans of the chest to establish the diagnosis. Artificial Intelligence (AI) based diagnosis through deep learning models for the classification of medical radiological images can play a very important role in the present scenario. However, for new infections like SARS-CoV-2 and its Variants of Concern (VOC) the labeled data may not be readily available for training a deep learning model while at the same time, the labeled images may be available for similar previously existing infections like viral or bacterial pneumonia. This study aims to propose a novel Semi-Supervised Domain Adaptation neural network, CoVSSDA to handle this limitation. CoVSSDA is an end-to-end deep convolutional neural network that is trained on the labeled images of the related previous infection with two classes {Normal, Pneumonia}, unlabeled images of the new infection with three classes {COVID19, Normal, Pneumonia} and a small batch of labeled images of the new infection such that the trained model acquires the knowledge about the novel class COVID19 and adapts to achieve an accuracy of 93.92% when tested for the new infection on the target domain and also outperforms other models based on domain adaptation.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126759564","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: 1

A Topographical Feature Extraction Approach for Classification of Soil Hyperspectral Image 土壤高光谱图像分类的地形特征提取方法

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596309

Sangeetha Annam, Anshu Singla

引用次数: 0

Compression using Matrix Folding Algorithm 利用矩阵折叠算法进行压缩

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596447

Gaurav Jindal, N. Sharma, Harshita Chadha, N. Pathak

{"title":"Compression using Matrix Folding Algorithm","authors":"Gaurav Jindal, N. Sharma, Harshita Chadha, N. Pathak","doi":"10.1109/icrito51393.2021.9596447","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596447","url":null,"abstract":"Space optimization is a particular phenomenon which has puzzled the computer programmers worldwide. More and more data needs to be stored into ever decreasing space. Several techniques are utilized to either decompress the files or better manage the memory. The first aspect involves the mathematics behind the conversion and the second aspect involves physics to store more data on to smaller chips. The proposed formulation i.e. The Matrix Folding Algorithm serves the first aspect of Space optimization by making use of mathematical calculations to reduce the size of a matrix. Matrix Folding Algorithm is a technique to reduce the space occupied by a square matrix to a level of 25% of the original size. The algorithm is based on mathematical calculations applied on the original values in order to double fold the matrix first from right to left and then from bottom to top. Like many other algorithms this one also has limitations that the original matrix should have numbers ranging from 0 through 15 only when we consider a system where the integer takes 2 bytes of space. And the matrix should have numbers ranging from 0 through 255 only when we consider a system where the integer takes 4 bytes of space.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123484573","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: 1

Collaborative Cloud Computing using Dedicated Shared Resources 使用专用共享资源的协同云计算

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596208

Sandeep Nandal, A. Chaudhary, A. Rana, Anil Kumar

引用次数: 1