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D7-R4: Software Development Life-Cycle for intelligent Vision Systems 11

D7-R4: Software Development Life-Cycle for Intelligent Visio...

引用

11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge engineering and Ontology Development (KEOD)

作者： Olszewska, J., I Univ West Scotland & UK Sch Comp & Engn Paisley Renfrew Scotland

ISBN: (纸本)9789897583827

intelligent Vision Systems (IVS) are omnipresent in our daily life from social media apps to m-health services, from street surveillance cameras to airport e-gates, from drones to companion robots. Hence, IVS encompass any software which has a visual input processed by means of algorithm(s) involving Artificial Intelligence (AI) methods. the design and development of these IVS softwares has become an increasingly complex task, since vision-based systems have evolved into (semi-)autonomous AI systems, usually requiring effective and ethical data processing along with efficient signal processing and real-time hardware/software integration as well as User Experience (UX) and (cyber)security features. Consequently, IVS system development necessitates an adapted software development life-cycle (SDLC) addressing these multi-domain needs, whilst being developer friendly. Hence, we propose in this paper a new SDLC we called D7-R4 which allows developers to produce quality, new-generation IVS to be deployed in real-time and in real-world, unstructured environments.

关键词： Expert Systems intelligent Vision Systems intelligent Agents intelligent Robotics Autonomous Systems Machine Vision Human-machine Cooperation Cybernetics Software engineering Software-hardware Design Software Development Life-Cycle

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Machine learning Based Improved Malware Detection Schemes

Machine Learning Based Improved Malware Detection Schemes

引用

international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Pradosh Priyadarshan Prateek Sarangi Adyasha Rath Ganapati Panda Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Silicon Institute of Technology India Siksha O Anusandhan (Deemed to be) University India Electronics and Telecommunications Engineering C V Raman Global University India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

In recent years, cyber security has become a challenging task to protect the networks and computing systems from various types of digital attacks. therefore, to preserve these systems, various innovative methods have been reported and implemented in practice. However, still more research work needs to be carried out to have malware free computing system. In this paper, an attempt has been made to develop simple but reliable ML based malware detection systems which can be implemented in practice. Keeping this in view, the present paper has proposed and compared the performance of three ML based malware detection systems applicable for computer systems. the proposed methods include k-NN, RF and LR for detection purpose and the features extracted comprise of Byte and ASM. the performance obtained from the simulation study of the proposed schemes has been evaluated in terms of ROC, Log loss plot, accuracy, precision, recall, specificity, sensitivity and F1-score. the analysis of the various results clearly demonstrates that the RF based malware detection scheme outperforms the model based on k-NN and LR the efficiency of detection of proposed ML models is either same or comparable to deep learning-based methods.

关键词： Radio frequency Deep learning Analytical models Feature extraction Malware Task analysis Standards

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A Novel Algorithmic Trading Approach Based on Reinforcement learning 11

A Novel Algorithmic Trading Approach Based on Reinforcement ...

引用

11th international conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Li Xucheng Peng Zhihao Dalian Neusoft Univ Informat Sch Comp & Software Dalian 116626 Peoples R China

ISBN: (纸本)9781728121659

Algorithmic trading has gained great popularity due to the rapidly increasing computing power of modern day computers. In order to reduce trading latency, market participants and academic researchers are constantly looking for better novel and successful approaches to help them to achieve greater success. In this paper, a novel reinforcement learning approach is proposed which defines the algorithmic trading problem under the framework of the classic reinforcement learning problem, aiming to optimize the agent's performance in an unknown environment. By using state-of-the-art techniques based on least-squares temporal difference learning, an algorithmic trading system is built to support the reinforcement learning process. Evaluation of the approach is done with data from the foreign exchange market and results shows that it is profitable, easy to be expanded in the future.

关键词： Reinforcement learning Algorithmic Trading Stock Exchange

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An Improved Multi-Instance learning Model for Postoperative Early Recurrence Prediction of Hepatocellular Carcinoma Using Histopathological Images 11th

An Improved Multi-Instance Learning Model for Postoperative...

引用

11th KES international conference on Innovation in Medicine and Healthcare, InMed 2023

作者： Zhan, Gan Wang, Fang Wang, Weibin Li, Yinhao Chen, Qingqing Hu, Hongjie Chen, Yen-Wei College of Information Science and Engineering Ritsumeikan University Kyoto Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China

ISBN: (纸本)9789819933105

HCC(Hepatocellular carcinoma) is one of the primary liver cancer around the world, and it produces a high mortality rate in clinical. Surgical resection is the first-line treatment choice for patients with HCC, but the problem is patients will still have a great chance of recurrence after surgery. therefore postoperative early recurrence prediction of HCC patients is necessary, which can guide physicians to manage the individualized follow-up so that we could increase the survival time of patients. MIL(multi-instance learning) model is mostly utilized in histopathological image analysis, and it has achieved promising results in most tasks, but it has one major limitation: the conventional MIL model loses the histopathological spatial structure information when generating the slide-level representation on flattened instance embeddings. In this light, we revisit the conventional MIL model and propose an improved MIL model to tackle the limitation. Our proposed method consists of three stages, the first stage is tissue patching, which is the same as the conventional MIL;the second stage is early recurrence probability heatmaps(ER-ProbMaps) generation, which is conducted by a well-trained patch-level prediction model on the tissue patches of the same slide;the final stage is to produce the slide-level recurrence prediction by a slide-level prediction model. We train the slide-level prediction model on obtained heatmaps to complete our postoperative early recurrence prediction task. Our experimental results show that our proposed improved MIL model can achieve superior performance than the conventional MIL model in our task. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tissue

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Prediction of Pneumonia Disease of Newborn Baby Based on Statistical Analysis of Maternal Condition Using Machine learning Approach

Prediction of Pneumonia Disease of Newborn Baby Based on Sta...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Md. Mehedi Hasan Md. Omar Faruk Bidesh Biswas Biki Md Riajuliislam Khairul Alam Syeda Farjana Shetu Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Pneumonia is one of the common diseases amongst children in Bangladesh. Many children die from pneumonia in Bangladesh. Pneumonia is an infection that infects the air sacs in one or both lungs. In Bangladesh, nearly 50,000 children die of pneumonia every year. For diseases forecasting, Machine learning algorithms are popular and used extensively. Machine learning allows us to fulfill such a task with much consequence. We established our dataset from the particular obtainable from our survey. For prognosticating pneumonia, we employed six traditional Machine learning algorithms. they are K- Nearest Neighbor (KNN), Naive Bayes classifier, Decision Tree, Support Vector Machine (SVM), Neural Network algorithm, and Random Forest. For implementing these algorithms, we applied Scikit-leam, Pandas, NumPy, and for visualizing our data, we have used Matplotlib and seaborn. By proper interpretation, we considered the best performing algorithm for the prediction of pneumonia. We have measured to classify whether pneumonia declines under pneumonia (Positive) and pneumonia (Negative) class. Among all the algorithms, we have chosen the best algorithm which is provided us best accuracy and F1-score. By the best accomplishing algorithm, our model can predict pneumonia quite well.

关键词： Support vector machines Pediatrics Machine learning algorithms Lung Prediction algorithms Classification algorithms Diseases

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Articulated Trajectory Mapping Using Mobile Environments for Location-Based learning 20

Articulated Trajectory Mapping Using Mobile Environments for...

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11th international conference on Advances in Information Technology, IAIT 2020

作者： Sasaki, Iori Arikawa, Masatoshi Takahashi, Akinori Graduate School of Engineering Sci. Akita University Akita Japan

ISBN: (纸本)9781450377591

this paper addresses how to enhance post-tour activities with digital mobile devices. Walkers sometimes use GPS loggers to record, review, and share their walking stories. However, the raw trajectory data recorded can be difficult to browse through on a map. To resolve this, based on our observations, we illustrated chunks of messy trajectories, inaccurate indoor points, and unstable trajectory lines as problems encountered when mapping raw trajectory data. then, we proposed a new framework that focuses on GPS horizontal accuracy to find indoor location points and on an accelerometer to find stopping points. We also applied the Douglas-Peucker algorithm to make the trajectory cleaner and then integrated the extracted points with the clean trajectory line. An implementation test demonstrated that the detection of the indoor location points and outdoor stopping points was done correctly, and the proposed system gave users a better experience for their review. the paper contributes to the research on cleaning up map-based displays of raw trajectory data by using not only location data but also other spatial information that smartphones can collect. © 2020 ACM.

关键词： Trajectories

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Geomancy: automated Performance Enhancement through data Layout Optimization 36

Geomancy: Automated Performance Enhancement through Data Lay...

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36th international conference on Massive Storage Systems and Technology, MSST 2020

作者： Bel, Oceane Chang, Kenneth Tallent, Nathan R. Duellmann, Dirk Miller, Ethan L. Nawab, Faisal Long, Darrell D.E. University of California Santa Cruz United States Pure storage United States Pacific Northwest National Labs United States CERN

Large distributed storage systems such as high-performance computing (HPC) systems used by national or international laboratories require sufficient performance and scale for demanding scientific workloads and must handle shifting workloads with ease. Ideally, data is placed in locations to optimize performance, but the size and complexity of large storage systems inhibit rapid effective restructuring of data layouts to maintain performance as workloads shift. To address these issues, we have developed Geomancy, a tool that models the placement of data within a distributed storage system and reacts to drops in performance. Using a combination of machine learning techniques suitable for temporal modeling, Geomancy determines when and where a bottleneck may happen due to changing workloads and suggests changes in the layout that mitigate or prevent them. Our approach to optimizing throughput offers benefits for storage systems such as avoiding potential bottlenecks and increasing overall I/O throughput from 11% to 30%. © 2020 36th international conference on Massive Storage Systems and Technology, MSST 2020. All Rights Reserved.

关键词： Digital storage

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Past-future Mutual Information Estimation in Sparse Information Conditions 11

Past-future Mutual Information Estimation in Sparse Informat...

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11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge Discovery and Information Retrieval (KDIR)

作者： Shalev, Yuval Ben-Gal, Irad Dept Ind Engn Lab AI Machine Learning Business & Data Analyt Tel Aviv Israel

ISBN: (纸本)9789897583827

We introduce the CT-PFMI, a context tree based algorithm that estimates the past-future mutual information (PFMI) between different time series. By applying a pruning phase of the context tree algorithm, uninformative past sequences are removed from PFMI estimation along with their false contributions. In situations where most of the past data is uninformative, the CT-PFMI shows better estimates to the true PFMI than other benchmark methods as demonstrated in a simulated study. By implementing CT-PFMI on real stock prices data we also demonstrate how the algorithm provides useful insights when analyzing the interactions between financial time series.

关键词： Past-future Mutual Information Context Tree Transfer Entropy Time Series Analysis

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Preventing Failures by Predicting Students' Grades through an Analysis of Logged data of Online Interactions 11

Preventing Failures by Predicting Students' Grades through a...

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作者： Cabral, Bruno Figueira, Alvaro Univ Porto CRACS INESCTEC Rua Campo Alegre 1021-55 Porto Portugal

ISBN: (纸本)9789897583827

Nowadays, students commonly use and are assessed through an online platform. New pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an elearning system for that purpose. However, although there can be intense feedback from these activities to students, usually it is restricted to the assessments of the online set of tasks. We propose a model that informs students of abnormal deviations of a "correct" learning path. Our approach is based on the vision that, by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student's current online actions towards the course. In the major learning management systems available, the interaction between the students and the system, is stored in log. Our proposal uses that logged information, and new one computed by our methodology, such as the time each student spends on an activity, the number and order of resources used, to build a table that a machine learning algorithm can learn from. Results show that our model can predict with more than 86% accuracy the failing situations.

关键词： data Mining e-learning Machine learning Online Interaction Comparison Prediction of Failures learning Management System

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APPLICATION OF MACHINE learning ALGORIthM IN OPTIMIZATION OF PSV FOR 110000DWT OIL TANKER 39

APPLICATION OF MACHINE LEARNING ALGORITHM IN OPTIMIZATION OF...

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ASME 39th international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Liu Xiyang Chen Jingpu Sun Wenyu Xu Wei China Ship Sci Res Ctr Shanghai Branch Shanghai Peoples R China

ISBN: (纸本)9780791884317

the pre-shrouded vane (PSV) in front of propeller is a kind of energy-saving device which can change the inflow to improve the received power of the propeller. the device needs to be optimized according to the flow field of the stern. Most of the existing design methods rely on the experience of the designer. In order to improve the design efficiency of PSV and obtain a design scheme with higher energy-saving effect, this paper presents an optimization and analysis method for PSV in front of propeller based on agent model. Aiming at an 110000dwt oil tanker, 11 design parameters such as stator angle and duct radius are determined by means of parameterization. the design parameters are sampled by Latin hypercube sampling method (LH), and the sample space with 300 samples is generated. the energy-saving effect of each sample is analyzed by CFD method. the data set is formed and next divided into training set and test set. then, machine learning methods are used to build the agent model of sample space. the error of each model in the test set is analyzed. To obtain the best model, the performance of several models in the test set and training set is considered. the applicability of different models is also highly considered. On this basis, the sensitivity analysis method is used to analyze the sensitivity of each design parameter. then, the main influencing parameters are found. Finally, particle swarm optimization and genetic algorithm are compared to optimize the design parameters of PSV for 110000dwt oil tanker. the optimization results are verified by CFD method. the results show that the artificial neural network model is better on this dataset, and the model error on test set is less than 1% compared with the CFD result. the optimal solution by genetic algorithm method is better than all the sample points, and a better design scheme of PSV is obtained.

关键词： intelligent optimization hydrodynamic energy-saving device machine learning tanker

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