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House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan

House Price Prediction using Machine Learning Algorithm - Th...

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Information Technology (ACIT)

作者： Maida Ahtesham Narmeen Zakaria Bawany Kiran Fatima Research Center for Computing Jinnah University for Women Karachi Pakistan

ISBN: (数字)9781728188553

ISBN: (纸本)9781728188560

House prices are a significant impression of the economy, and its value ranges are of great concerns for the clients and property dealers. Housing price escalate every year that eventually reinforced the need of strategy or technique that could predict house prices in future. There are certain factors that influence house prices including physical conditions, locations, number of bedrooms and others. Traditionally predictions are made on the basis of these factors. However such prediction methods require an appropriate knowledge and experience regarding this domain. Machine Learning techniques have been a significant source of advanced opportunities to analyze, predict and visualize housing prices. In this paper, Gradient Boosting Model XGBoost is utilized to predict housing prices. Publicly available dataset containing 38,961 records of Karachi city is attained from an Open Real Estate Portal of Pakistan. Lot of work has been done in predicting house prices across many countries, however very limited amount of work has been done for predicting house prices in Pakistan. Our proposed house price prediction model is able to predict 98% accuracy.

关键词： Urban areas Predictive models Machine learning algorithms Machine learning Prediction algorithms Numerical models data models

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Automatically localising ROIs in hyperspectral images using background subtraction techniques

Automatically localising ROIs in hyperspectral images using ...

引用

international conference on Image and Vision Computing New Zealand, IVCNZ

作者： Munir Shah Vanessa Cave Marlon dos Reis AgResearch Christchurch New Zealand

ISBN: (数字)9781728185798

ISBN: (纸本)9781728185804

The use of snapshot hyperspectral cameras is becoming increasingly popular in agricultural scientific studies. One of the key steps in processing experimental hyperspectral data is to precisely locate the sample material under study and separate it from other background material, such as sampling instruments or equipment. This is very laborious work, especially for hyperspectral imaging scenarios where there might be a few hundred spectral images per sample. In this paper we propose a multiple-background modelling approach for automatically localising the Regions of Interest (ROIs) in hyperspectral images. The two key components of this method are i) modelling each spectral band individually and ii) applying a consensus algorithm to obtain the final ROIs for the whole hyperspectral image. Our proposed approach is able to achieve approximately a 14% improvement in ROIs detection in hyperspectral images compared to traditional video background modelling techniques.

关键词： Subtraction techniques Cameras data models Sensors Reliability Hyperspectral imaging Testing

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Illicit Activity Detection in Large-Scale Dark and Opaque Web Social Networks

Illicit Activity Detection in Large-Scale Dark and Opaque We...

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IEEE international conference on Big data

作者： Dhara Shah T.G. Harrison Christopher B. Freas David Maimon Robert W. Harrison Georgia State University Atlanta USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Many online chat applications live in a grey area between the legitimate web and the dark net. The Telegram network in particular can aid criminal activities. Telegram hosts “chats” which consist of varied conversations and advertisements. These chats take place among automated “bots” and human users. Classifying legitimate activity from illegitimate activity can aid law enforcement in finding criminals. Social network analysis of Telegram chats presents a difficult problem. Users can change their username or create new accounts. Users involved in criminal activity often do this to obscure their identity. This makes establishing the unique identity behind a given username challenging. Thus we explored classifying users from their language usage in their chat *** volume and velocity of Telegram chat data place it well within the domain of big data. Machine learning and natural language processing (NLP) tools are necessary to classify this chat data. We developed NLP tools for classifying users and the chat group to which their messages belong. We found that legitimate and illegitimate chat groups could be classified with high accuracy. We also were able to classify bots, humans, and advertisements within conversations.

关键词： Social networking (online) Law enforcement Machine learning Big data Tools Natural language processing data models

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Quality control system of automobile bearing production based on edge cloud collaboration

Quality control system of automobile bearing production base...

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international conference on Advanced Mechatronic Systems (ICAMechS)

作者： Yixiong Feng Chen Yang Tianyue Wang Hao Zheng Yicong Gao Wei Fan State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China China Unicom (Zhejiang) Industrial Internet Co. Ltd Hangzhou China Hangzhou Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9781728165301

ISBN: (纸本)9781728165318

In view of the strict quality control and traceability and high equipment management requirements in the current production process of automobile bearing products, a quality control system for shaft products based on the collaboration of edge cloud is proposed. This system realizes the data collection and analysis of various production factors in the bearing processing and assembly process through the workshop Internet of things, the storage, processing and analysis of multi-source heterogeneous data through the cloud platform, and the quality control and optimization of bearing products, production equipment management and optimization, product quality management and traceability functions through the micro service architecture. This paper proposes an edge cloud collaborative system architecture based on multi-source data acquisition and fusion technology, equipment anomaly warning technology and edge computing technology. The experimental results show that the system realizes the intelligent control of the whole process of bearing production, greatly improves the product quality and production efficiency, and provides a new mode for digital and intelligent bearing production process.

关键词： Production Cloud computing Quality control Process control Collaboration data models Shafts

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Lung CT image aided detection COVID-19 based on Alexnet network

Lung CT image aided detection COVID-19 based on Alexnet netw...

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Communication, Image and Signal processing (CCISP), international conference on

作者： Tao Wang Yongguo Zhao Lin Zhu Guangliang Liu Zhengguang Ma Jianghua Zheng Academy of Sciences Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology Qilu University of Technology (Shandong Academy of Sciences) Institute of automation Shandong ShanDong China

ISBN: (数字)9781728185897

ISBN: (纸本)9781728185903

In this article, we use the Alexnet network in deep learning to determine whether the lung CT images are infected with *** of all, in the data preprocessing stage, the original CT image data is scaled and normalized to reduce noise interference. The batch operation of training set and test set can increase the training speed;Secondly, build an eight-layer Alexnet network model, set reasonable hyperparameters for each layer of the network, define the loss function and optimizer, and use the processed data to train the weight parameters of each layer in the network ***, three indicators of accuracy, accuracy and recall are used to quantify the effect of model classification, and the impact of different training times on these three indicators is compared to select the best classification *** the same time, use pyqt5 to write the corresponding auxiliary detection interface to facilitate the display of test results and the selection of classification *** construction of the network model, the definition of the loss function and the definition of the optimizer are all based on the Pytorch deep learning framework.

关键词： Training COVID-19 Computed tomography data models Lung Feature extraction Robots

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Multi-Agent Technology for Industrial Applications: Barriers and Trends

Multi-Agent Technology for Industrial Applications: Barriers...

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IEEE international conference on Systems, Man and Cybernetics

作者： Vladimir Marik Vladimir Gorodetsky Petr Skobelev CIIRC Czech Technical University Prague Czech Republic St. Petersburg State Electrotechnical University St. Petersburg Russia Institute of Automation and Information Technology Samara State Technical University Samara Russia

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Multi-agent systems (MAS) have been an area of high expectations of the industrial IT community. However, in reality, these expectations are still not met and, in practice, the industry very rarely uses the MAS design methodologies, technologies, and software tools despite the appearance of many new classes of applications for which the MAS paradigm could be the perfect match. This paper analyzes the barriers and trends of the mismatch between the recent industrial anticipations and the real state of the practical use of MAS. It identifies engineering problems with very little re-use of code that currently stops economics of scale and impedes the extensive industrial MAS deployment and the ways to overcome them.

关键词： Industries Biological system modeling Software algorithms Tools Market research data models Software tools

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Frequent Itemset Mining with Hadamard Response Under Local Differential Privacy

Frequent Itemset Mining with Hadamard Response Under Local D...

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international conference on Electronics Information and Emergency Communication

作者： Haijiang Liu Xiangyu Bai Xuebin Ma Lianwei Cui Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Inner Mongolia University Hohhot China Inner Mongolia Big Data Development Authority Hohhot China

ISBN: (数字)9781728163130

ISBN: (纸本)9781728163147

Frequent itemset mining is a basic data mining task and has many applications in other data mining tasks. However, users’ personal privacy information will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. data mining using local differential privacy involves two major problems. The first one is that the accuracy of the results after mining is low, and the other one is that the user transmits a large amount of data to the server, which results in higher communication costs. In this study, we demonstrate that the Hadamard response (HR) algorithm improves the accuracy of the results and reduces the communication cost from k to log k. Finally, we use the Frequent pattern tree (FP-tree) algorithm for frequent itemset mining to compare the existing algorithms.

关键词： Itemsets Perturbation methods Privacy Task analysis

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An Innovative Framework for Supporting Remote Sensing in Image processing Systems via Deep Transfer Learning

An Innovative Framework for Supporting Remote Sensing in Ima...

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IEEE international conference on Big data

作者： Oxana Korzh Ashish Sharma Mikel Joaristi Edoardo Serra Alfredo Cuzzocrea Nvidia USA Boise State University USA iDEA Lab University of Calabria Italy LORIA France

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

In this work, we propose a method based on Deep-Learning and Convolutional Neural Network (CNN) ensemble fine-tuning for the task of remote sensing imagery registration and processing. Our method is based on the CNN transfer learning technique that allows the use of large-scale models that are already pre-trained on big general datasets and fine-tunes them for a particular application area. This approach can significantly decrease the needed size of the training set, for cases where such big training datasets are not available, and improve the quality of classification using a larger CNN or an ensemble of CNNs. This paper addresses the challenges encountered at each stage of the proposed pipeline. For image registration, objects of predefined type are detected, such as roads with hardcover and buildings using a CNN ensemble. Also, a CNN ensemble is used to detect undesirable structures in the image, such as clouds or rocks on the agricultural fields. Our image segmentation method can be used for image matching and fusion. To test our approach, we use an annotated dataset from the Kaggle contest "Dstl Satellite Imagery Feature Detection," UC Merced Land Use dataset, and a custom annotated dataset of remote sensing imagery of agricultural areas.

关键词： Training Image registration Transfer learning Big data Rocks Task analysis Remote sensing

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Analytics-oriented Preventive Care for Communicable Disease

Analytics-oriented Preventive Care for Communicable Disease

引用

international conference on Advanced Learning Technologies (ICALT)

作者： Liliana Quyen Tang Rafael Pinto Vivekanandan Kumar Athabasca University AB Canada

ISBN: (数字)9781728160900

ISBN: (纸本)9781728160917

Today, most sexually transmitted infections (STIs) can be cured. However, stigma around STIs - especially fears over negative social attitudes - prevents many people from getting tested or notifying their sexual partners that they are infected. The Government of Alberta invests considerable resources on strategies to improve the health of Albertans. However, the massive outbreak of epidemics like syphilis indicate there are huge gaps in the current approach. This paper proposes the development of a centralized information system to correlate the areas of edu-communication and epidemiology as well as employs techniques in predictive analytics to explore the correlation between certain risk factors and the change in the number of infectious syphilis cases in the province of Alberta. The performance of the resultant models provides a proof-of-concept for the preventive strategies proposed in the paper.

关键词： Government Machine learning Public healthcare Support vector machines Drugs Machine learning algorithms data mining

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Exploiting Cliques for Granular Computing-based Graph Classification

Exploiting Cliques for Granular Computing-based Graph Classi...

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international Joint conference on Neural Networks (IJCNN)

作者： Luca Baldini Alessio Martino Antonello Rizzi Department of Information Engineering Electronics and Telecommunications University of Rome "La Sapienza" Rome Italy

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The most fascinating aspect of graphs is their ability to encode the information contained in the inner structural organization between its constituting elements. Learning from graphs belong to the so-called Structural Pattern Recognition, from which Graph Embedding emerged as a successful method for processing graphs by evaluating their dissimilarity in a suitable geometric space. In this paper, we investigate the possibility to perform the embedding into a geometric space by leveraging to peculiar constituent graph substructures extracted from training set, namely the maximal cliques, and providing the performances obtained under three main aspects concerning classification capabilities, running times and model complexity. Thanks to a Granular Computing approach, the employed methodology can be seen as a powerful framework able to synthesize models suitable to be interpreted by field-experts, pushing the boundary towards new frontiers in the field of explainable AI and knowledge discovery also in big data contexts.

关键词： Feature extraction Kernel Computational modeling data mining Training Complexity theory

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