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50th Annual Convention of computer Society of India : Big Data Analytics, CSI 2015

作者： Saini, Sonia Kohli, Shruti Birla Institute of Technology Ranchi India Faculty of Computer Science & Engineering Birla Institute of Technology Ranchi India

ISBN: (纸本)9789811066191

Big Data Analysis in the healthcare domain is an upcoming and nascent topic. The data that can be analyzed from the healthcare domain is typical of huge volume and is quite varying in nature. The Electronic Health Records (EHRs) of just one year for a major hospital can typically run into terabytes and since these records are both structured as well as unstructured, they are a good fit for being analyzed using Big Data tools like Hadoop, R, and Python, etc. Twitter, with its brevity of messages, is one the sources of fast moving information. Real-time information sharing has made healthcare organizations, hospitals, medical institutes, research companies to come out with their respective Twitter "handle", from which the respective organization can share it’s official information, can be reached for information, clarifications, and even grievance redress. Such all information is unstructured with links, video, text all being shared and proper data analysis tools are required to gather meaningful data from the tweets. In this paper, we focus on Twitter (Social) data analysis using R and MongoDB. We discuss the data analysis packages available in R, which can be used to analyze tweet and EHR data. We discuss how MongoDB helps map tweets to documents, provides basic operations like aggregation and what all analysis features/packages R provides for analyzing the medical domain data. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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Enhanced model for cloud data security based on searchable encryption and hybrid fragmentation

Enhanced model for cloud data security based on searchable e...

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2019 International Conference on computer, Control, Electrical, and Electronics Engineering, ICCCEEE 2019

作者： Awad, Abdelrhman Sayed Yousif, Adil Kadoda, Gada University of Science and Technology Faculty of Computer Science Khartoum Sudan Najran University Faculty of Arts Science Sharourah Saudi Arabia University of Khartoum Faculty of Mathematical Sciences Khartoum Sudan

ISBN: (纸本)9781728110066

Cloud computing is a new technology that transfer the computing process from personal computers into cloud servers over the internet. Nevertheless, as the client information data is stored in the cloud provider servers, the confidentiality of the information become a new concern. Different algorithms based on Encryption is presented previously to provide cloud clients with confidentiality. The main idea of encryption algorithms for cloud data is to permit cloud clients queries to be handled using encrypted data without decryption. This paper presents a new security mechanism using hybrid method of encryption algorithms and a distribution system to enhance cloud database confidentiality. A vertical fragmentation technique is adopted from alsirhani's model for distributing data over clouds. However, to overcome a weakness in alsirhani's model where compromises to a fragment can still make data meaningful. Instead, the proposed model uses a hybrid fragmentation technique to make data on fragments meaningless if compromised. The proposed model distributes the cloud database among the clouds using the provider views and level of confidentiality that is delivered by the employed encryption algorithms. To evaluate the proposed searchable encryption and hybrid fragmentation model, the study developed a Java application for simulating the hybrid cloud. The simulation combines public and private clouds;as essential processes is conducted inside the private cloud. The evaluation of the work was conducted by comparing the proposed model with existing solutions in query response and security characteristics. Preliminary results showed that the proposed searchable encryption and hybrid fragmentation model provides a secure mechanism that enhances data confidentiality in terms of faster response and additional security. © 2019 IEEE.

关键词： Personal computers

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SNN-SB: Combining Partial Alignment Using Modified SNN Algorithm with Segment-Based for Multiple Sequence Alignments 1

SNN-SB: Combining Partial Alignment Using Modified SNN Algor...

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1st International Conference on Engineering and technology, ICoEngTech 2021

作者： Ali, Aziz Nasser Boraik Hassan, Hassan Pyar Ali Bahamish, Hesham Department of Information Technology Computing Information Technology Faculty University of Science and Technology Yemen Faculty of Environmental Science and Marine Biology Hadhramout University Yemen Computer Science Department Collage of Computers and Information Technology Hadhramout University Yemen

Multiple sequence alignment (MSA) is an essential tool in the area of bioinformatics. Many MSA algorithms have been proposed last decade, however there is still opportunity for improvement in accuracy. Including partial alignment into MSA has been proved to be an effective approach to improve the quality of results of final Multiple sequence alignment. This paper presents a novel algorithm known as SNN-SB, which used to detect the related residue of protein sequences to build partial alignments using modified Shared Near Neighbors algorithm and segment-based alignments. The partial alignment is used as guide for DIALIGN-TX algorithm to build the final MSA. In order to evaluate the effectiveness of the SNN-SB, we compared the final result with 10 outstanding MSA tools. The results of SNN-SB got the maximum mean Q score and mean SP score on IRMBASE 2.0 benchmark. Moreover, it got around 8% improvement in BAliBASE 3.0 benchmark regarding to Q score compared to DIALIGN-TX. © Published under licence by IOP Publishing Ltd.

关键词： Bioinformatics

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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Engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Efficient Method for Detecting Abnormal Growth of Blood Vessels Using Convolutional Neural Network

Efficient Method for Detecting Abnormal Growth of Blood Vess...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Kumar, A. Deepak Sasipraba, T. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600 119 India Department of Computer Science and Engineering St. Joseph s Institute of Technology Chennai600119 India Faculty of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350334357

Diabetic Retinopathy is the main problem in human life because of high sugar levels present in the blood. Various organs get affected due to this reason. The eye is the one of the parts of the human eye which goes to vision problems sometimes blindness. The early stages of detection need to protect the human eye. Existing model uses various methods which do not solve the problem completely. Machine learning based approach introduced for detection of the affected area of eye. The Blood Vessels of the eye get affected as a result bleeding in the eye and excess growth in the eye. Traditional algorithms are not suitable for detecting this growth rate due to the less resolution images. The CNN based model with integrated data sets are used to classify and detect the blood vessel. The high resolution images are used for detecting the location and exact difference in normal vessels. Various algorithms are used with different data sets for making multidimensional analysis. The objective of this method is to identify the heterogeneous vessels from the normal vessel with a high level of accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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Residential demand-side flexibility provision under a multi-level segmented tariff

Residential demand-side flexibility provision under a multi-...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Li, Na Bruninx, Kenneth Tindemans, Simon Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350396782

This paper proposes a multi-level segmented tariff to encourage consumers to provide demand response using a battery. The aim of the tariff is to (i) properly reflect consumers’ contribution to the distribution grid cost while ensuring cost recovery for the distribution network operator and (ii) to provide consumers with a financial incentive to flatten their load profile and avoid peak demand. An optimization problem is formulated to describe how consumers can provide demand response by managing their batteries. To evaluate the effectiveness of the proposed multi-level segmented tariff, four case studies were conducted. The results indicate that the multi-level segmented tariff is the most effective in reducing coincident peak demand, with a reduction of 22%. Policymakers and regulators are recommended to consider multi-level segmented distribution tariffs, as it provides an incentive to consumers to manage their assets to provide demand response. © 2023 IEEE.

关键词： Cost recovery Cost reflectivity Demand-side flexibility Distribution network Multi-level segmented tariff

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An integrated information service platform for wetlands based on WebGIS

Journal of Software

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Journal of Software 2012年第10期7卷 2278-2285页

作者： Kou, Weili Yue, Cairong Gan, Shu School of Computer and Information Southwest Forestry University Kunming China Faculty of Land Resource Engineering Kunming University of Science and Technology Kunming China School of Forestry Southwest Forestry University Kunming China Faculty of Land Resource Engineering Kunming University of Science and Technology Kunming China

Wetland resource management is an important task in the protection and utilization of wetlands. Given the environmental interests in these wetlands, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of wetlands. Our objective is to develop an interactive WebGIS and geodatabase for wetlands providing mapping and data services. This paper represents an integrated information service platform based on WebGIS using spatial statistics and analysis method based on ArcGIS Server to manage and utilize wetland resources. The system adopts the Browser/Server architecture, dot NET framework, and ArcGIS Server component to improve the performance of current system. The functions of this system are map distributing, query and display, spatial statistics and analysis, scientific education, remote management, and wetland resource sharing. It has much superiority like good compatibility, convenient data maintenance, high efficiency, and code security, etc. The system scalability and database stability have been improved greatly. Practical application shows that the system can fully satisfy the requirement of wetland resource management and achieves expected goals. © 2012 ACADEMY PUBLISHER.

关键词： Wetlands

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Fuzzy support vector machine based on non-equilibrium data

Fuzzy support vector machine based on non-equilibrium data

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Tian, Da-Zeng Peng, Gui-Bing Ha, Ming-Hu Faculty of Physics Science and Technology Hebei University Baoding 071002 China Faculty of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781467314855

Fuzzy support vector machine (FSVM), whose membership function is based on class centers, can effectively solve the problem that the traditional support vector machine (SVM) is sensitive to the noises and outliers. However, FSVM assigns smaller memberships to support vectors, which may decrease the effects of these support vectors upon the construction of classification hyperplane. At the same time, FSVM has some disadvantages in dealing with the non-equilibrium data classification. Therefore, a novel method to determine membership function is proposed, and a new FSVM based on non-equilibrium data is constructed. Experiments show that the new FSVM can effectively reduce the misclassification rates produced by the class with fewer samples in dealing with non-equilibrium data classification problem. Therefore, the proposed FSVM may make the misclassification rates upon two classes approximately equal. © 2012 IEEE.

关键词： Classification (of information)

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eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers 5

eRock at Qur’an QA 2022: Contemporary Deep Neural Networks ...

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5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, OSACT 2022

作者： Aftab, Esha Malik, Muhammad Kamran Department of Computer Science Department of Information Technology Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9791095546757

Question Answering (QA) has enticed the interest of NLP community in recent years. NLP enthusiasts are engineering new Models and fine-tuning the existing ones that can give out answers for the posed questions. The deep neural network models are found to perform exceptionally on QA tasks, but these models are also data intensive. For instance, BERT has outperformed many of its contemporary contenders on SQuAD dataset. In this work, we attempt at solving the closed domain reading comprehension Question Answering task on QRCD (Qur’anic Reading Comprehension Dataset) to extract an answer span from the provided passage, using BERT as a baseline model. We improved the model’s output by applying regularization techniques like weight-decay and data augmentation. Using different strategies we had 59% and 31% partial Reciprocal Ranking (pRR) on development and testing data splits respectively. © European Language Resources Association (ELRA).

关键词： Natural language processing systems

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Feature Selection and Rule Extraction Based on Variable Precision Rough Set 3

Feature Selection and Rule Extraction Based on Variable Prec...

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3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Kesuma, I.A. Zarlis, M. Nababan, E.B. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia

Main factors in feature selection is dimensionality in data and decision making on partial information handling as precision classification errors. This study will demonstrate the feature selection using the rough set method, especially in handling missing information along with an ambiguous decision system with a precision variable-based approach to finding significant features and extracting the required rules. The experiment was carried out with 12 Dermatology datasets from the UCI Machine Learning Repository consisting of 11 conditional attributes and a decision variable. The experimental results show the results of the dominant conditional feature selection of 45% of the overall existing conditional features, along with more concise rules based on selected features. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Feature Selection

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