Slope Processes, Mass Movement and Soil Erosion: A Review

La densitometrie osseuse dans une population libanaise de 858 femmes et 165 hommes entre 20 et 79 ans a ete une population americaine et europeenne.

Bone mineral density of the Lebanese reference population

Trends of crimes in India keep changing with the growing population and rapid development of towns and cities. The rise in crimes at any place especially crimes against women, children and weaker sections of the society is a worrying factor for the Indian Government. In India, the crime data is maintained by National Crime Records Bureau as well as an application called Crime Criminal Information System is available to make inquiry and generate reports for the crime data. To curb crime, the Police need countless hours to go through the crime data and determine the various factors that affect it. Therefore, there is necessity of tools which can automatically predict the factors that effects the crimes effectively and efficiently. The field of machine learning has emerged in the recent years for this purpose. In this paper, various machine learning techniques have been applied on crime data to monitor the impact of economic crisis on the crime in India. The effect of unemployment rates and Gross District Domestic Product on theft, robbery and burglary has been monitored across districts of various states in India. Further, Granger causality between crime rates and economic indicators has also been calculated. It has been observed from the experimental work that unemployment rate is the major economic factor which affects the crime rate, thus paving the path to control the crime rate by raising more opportunities for the employment.

Monitoring the Impact of Economic Crisis on Crime in India Using Machine Learning

This paper gives an across-the-board comprehensive review and survey of the most prominent studies in the field of Urdu optical character recognition (OCR). This paper introduces the OCR technology and presents a historical review of the OCR systems, providing comparisons between the English, Arabic, and Urdu systems. Detailed background and literature have also been provided for Urdu script, discussing the script’s past, OCR categories, and phases. This paper further reports all state-of-the-art studies for different phases, namely, image acquisition, pre-processing, segmentation, feature extraction, classification/recognition, and post-processing for an Urdu OCR system. In the segmentation section, the analytical and holistic approaches for Urdu text have been emphasized. In the feature extraction section, a comparison has been provided between the feature learning and feature engineering approaches. Deep learning and traditional machine learning approaches have been discussed. The Urdu numeral recognition systems have also been deliberated concisely. The research paper concludes by identifying some open problems and suggesting some future directions.

Urdu Optical Character Recognition Systems: Present Contributions and Future Directions

Nowadays crimes are increasing at a high rate which is a great challenge for the police department of a city. A huge amount of data on different types of crimes taking place in different geographic locations is collected and stored annually. It is highly essential to analyze data so that potential solutions for solving and mitigating the crime incidents and predicting similar incident patterns for future becomes possible. Then it can be carried out using big data and various machine learning techniques in conjunction. The paper introduced a solution to the crime prediction problem using Naive Bayes classifier, which includes finding the most likely criminal of a particular crime incident when the history of similar crime incidents has been provided with the incident-level crime data. The incident-level crime data is provided as a crime dataset which includes incident date and location, crime type, criminal ID and the acquaintances are the attributes or crime parameters. The acquaintances are the suspects whose names are either directly involved in the incident or indirectly the acquaintances of the criminal. Acquiring a real-time crime dataset is a difficult process in practice due to confidentiality principle. So, crime dataset are used for the inputs using the state of the art methods. The proposed system is tested for the crime prediction problem using the data learning, and the experimental results show that the proposed system provides better results and finding of the potential solutions and crime patterns.

Analysis and prediction of crime patterns using big data

The most important concern in the medical domain is to consider the interpretation of data and perform accurate diagnosis. To improve diagnostic process and avoid misdiagnosis, many e-Health systems use artificial intelligence method and especially artificial neural network to manipulate diverse type of clinical data. A common bone disease ‘osteoporosis’ does not only depend on bone mineral density but also some other factors have significance i.e. age, weight, height, life-style etc. these all factors play considerable role to diagnosis osteoporosis. In this study, we propose a decision making system using the factors other then bone mineral density to provide a convenient, accurate and inexpensive solution to predict future fracture risk.

Artificial Neural Network: A Tool for Diagnosing Osteoporosis

Nowadays crime is one of major threats faced by our government .Extensive research in criminology has been done on focusing the study of crime and criminal behavior scientifically. It is one of most important field where the applications of data mining techniques are producing fruitful results. Data mining has been using to model crime detection and classification problems. Manually addressing the large amount of the volume of crime that is being committed makes crime prevention strategies a time consuming and complex task. In this paper data mining techniques are examined to predict crime and criminality. We apply machine learning algorithms to a dataset of criminal activity to predict attributes and event outcomes. We will also do comparative analysis between different classification techniques..

http://www.isca.in/rjrs/archive/v4/i3/15.ISCA-RJRS-2013-1005.pdf

Application of Machine learning Algorithms in Crime Classification and Classification Rule Mining

The word ‘Climate Change” is not as simple and small in meaning as it seen. It exhibits dangerous and alarming consequences. Climate changes is continuous process from East to West, North to South, Soil to Sky, Water to Air, Forest to Desert, Ocean to Mountains, in fact everywhere. Climate change Monitoring (CCM) is the phenomena to observe and record climate changes and put forward them for decision makers or domain experts to prevent environment, avoid or at least prepare for different kinds of natural disasters, to prevent contamination of various precious natural resources including water, air and soil etc. Rapid advancement of Information and Communication Technology (ICT) is blessing for mankind and incorporation of this revolutionary technology in serving mankind in diverse domain is remarkable. Use of ICT in climate change mitigation, adoption and monitoring not only replacing the conventional techniques and systems also adding great accuracy, reliability and flexibility also provide diverse choices in related domain. ICT Technology is contributing in variety of domain, similarly these fruitful technologies using in Climates changes observation, Climate changes monitoring and climates changes adaptation as well as in Disaster Management. ICT based application can help in reducing climate changes impacts on environment. By using ICT in climate monitoring specially, provide real time observation, reduce cost, decrease power consumption, lively tracking, real time data processing and analysis etc. This review study evaluates how widely ICT and ICT based applications can be used in mitigation, adoption and monitoring of climate changes in developed and developing countries.

http://www.isca.in/rjrs/archive/v3/i12/18.ISCA-RJRS-2013-1003.pdf

Role of ICT in Climate Change Monitoring: A review Study of ICT based Climate Change Monitoring Services

Osteoporosis is the “Silent disease” in which the microarchitecture of bones becomes weak due to the low bone mass density. This common public health problem has lack of symptoms, and at the moment can only be diagnosed using a technique called dual-energy x-ray absorptiometry (DEXA). The DEXA scan of a bone provides bone mineral density (BMD), which is compared against the standard BMD values for diagnoses. The availability of DEXA machine in a third world country like Pakistan is an issue because of its high cost and the high percentage of population living in rural areas with limited health care facilities. The aim of this paper is to review the published work on this subject and suggest a solution that aids early diagnosis of osteoporosis using x-ray radiographs only. An associated problem is the use of T-score and Z-score reference values that are not based on the local population.

http://www.isca.in/rjrs/archive/v2/i3/15.ISCA-RJRS-2012-438.pdf

Bone Mineral Density Correlation against Bone Radiograph Texture Analysis: An Alternative Approach

Face recognition is one of the most important computer vision problems. Its importance is largely due to the security issues the world is facing at the moment and also the requirement of a more robust system security standard. This work investigates the use of facial weighted distance transform to improve the face recognition rate. Weighted distance transform, also known as geodesic distance, not only considers the spatial distance among pixels but also takes into account the local intensity variations providing a distance transform in the spatiointensity domain. Geodesic distance transform of facial images is estimated using the “Fast Marching” [1, 2] technique which is based on Dijkstra's algorithm employed to identify the shortest network path. It is a single pass algorithm providing efficient geodesic distance feature vector, thereby reducing the recognition time. A standard Frontal Face Data Base [3] is used to validate the algorithm. The obtained results are comparable to the state-of-the-art face recognition techniques.

http://www.isca.in/rjrs/archive/v3/i9/4.ISCA-RJRS-2013-884.pdf

Abdul Basit Shaikh

Papers

Artificial Neural Network: A Tool for Diagnosing Osteoporosis

Application of Machine learning Algorithms in Crime Classification and Classification Rule Mining

Role of ICT in Climate Change Monitoring: A review Study of ICT based Climate Change Monitoring Services

Bone Mineral Density Correlation against Bone Radiograph Texture Analysis: An Alternative Approach

Face recognition using weighted distance transform