New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance

Human action recognition is an active research area in computer vision because of its immense application in the field of video surveillance, video retrieval, security systems, video indexing and human computer interaction. Action recognition is classified as the time varying feature data generated by human under different viewpoint that aims to build mapping between dynamic image information and semantic understanding. Although a great deal of progress has been made in recognition of human actions during last two decades, few proposed approaches in literature are reported. This leads to a need for much research works to be conducted in addressing on going challenges leading to developing more efficient approaches to solve human action recognition. Feature extraction is the main tasks in action recognition that represents the core of any action recognition procedure. The process of feature extraction involves transforming the input data that describe the shape of a segmented silhouette of a moving person into the set of represented features of action poses. In video surveillance, global moment invariant based on Geometrical Moment Invariant (GMI) is widely used in human action recognition. However, there are many drawbacks of GMI such that it lack of granular interpretation of the invariants relative to the shape. Consequently, the representation of features has not been standardized. Hence, this study proposes a new scheme of human action recognition (HAR) with geometrical moment invariants for feature extraction and supervised invariant discretization in identifying actions uniqueness in video sequencing. The proposed scheme is tested using IXMAS dataset in video sequence that has non rigid nature of human poses that resulting from drastic illumination changes, changing in pose and erratic motion patterns. The invarianceness of the proposed scheme is validated based on the intra-class and inter-class analysis. The result of the proposed scheme yields better performance in action recognition compared to the conventional scheme with an average of more than 99% accuracy while preserving the shape of the human actions in video images

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Support Vector Machine Algorithm for SMS Spam Classification in The Telecommunication Industry

In recent years, we have withnessed a dramatic increment volume in the number of mobile users grows in telecommunication industry. However, this leads to drastic increase to the number of spam SMS messages. Short Message Service (SMS) is considered one of the widely used communication in telecommunication service. In reality, most of the users ignore the spam because of the lower rate of SMS and limited amount of spam classification tools. In this paper, we propose a Support Vector Machine (SVM) algorithm for SMS Spam Classification. Support Vector Machine is considered as the one of the most effective for data mining techniques. The propose algorithm have been evaluated using public dataset from UCI machine learning repository. The performance achieved is compared with other three data mining techniques such as Naïve Bayes, Multinominal Naïve Bayes and K-Nearest Neighbor with the different number of K= 1,3 and 5. Based on the measuring factors like higher accuracy, less processing time, highest kappa statistics, low error and the lowest false positive instance, it’s been identified that Support Vector Machines (SVM) outperforms better than other classifiers and it is the most accurate classifier to detect and label the spam messages with an average an accuracy is 98.9%. Comparing both the error parameter overall, the highest error has been found on the algorithm KNN with K=3 and K=5. Whereas the model with less error is SVM followed by Multinominal Naïve Bayes. Therefore, this propose method can be used as a best baseline for further comparison based on SMS spam classification

The Comprehensive Review of Neural Network: An Intelligent Medical Image Compression for Data Sharing

In the healthcare environment, digital images are the most commonly shared information. It has become a vital resource in health care services that facilitates decision-making and treatment procedures. The medical image requires large volumes of storage and the storage scale continues to grow because of the advancement of medical image technology. To enhance the interaction and coordination between healthcare institutions, the efficient exchange of medical information is necessary. Therefore, the sharing of the medical image with zero loss of information and efficiency needs to be guaranteed exactly. Image compression helps ensure that the purpose of sharing this data from a medical image must be as intelligent as possible to contain valuable information while at the same time minimizing unnecessary diagnostic information. Artificial Neural Network has been used to solve many issues in the processing of images. It has proved its dominance in the handling of noisy or incomplete image compression applications over traditional methods. It contributes to the resulting image by a high compression ratio and noise reduction. This paper reviews previous studies on the compression of intelligent medical images with the neural network approach to data sharing

Modelling contents status for IPTV delivery networks

Since IPTV has been invented, IPTV is considered a dominant technology to distribute high quality videos and live channels anytime anywhere over challenging environment to end users who are having different preferences and demands. Presently, IPTV service providers manage IPTV delivery networks, in terms of contents, channels, resources, based on contents popularity distribution and/or users’ preferences only. Although content popularity and users’ preferences play an important role to cope with the increasing demand of IPTV contents/channels, these two measures fail in producing efficient IPTV delivery networks For that, IPTV delivery network designing should integrate the IPTV content characteristics like size, interactivity, the rapid changing lifetime. Therefore, the idea of this paper is to build a mathematical model that integrates all these factors in one concept called IPTV content status. Modeling the contents status according to its characteristics is an important point to design Content-Aware IPTV delivery networks.The experimental results showed the superiority of modeling IPTV content status in balancing the load and reducing the resources waste

Comparison of hash function algorithms against attacks: a review

Hash functions are considered key components of nearly all cryptographic protocols, as well as of many security applications such as message authentication codes, data integrity, password storage, and random number generation. Many hash function algorithms have been proposed in order to ensure authentication and integrity of the data, including MD5, SHA-1, SHA-2, SHA-3 and RIPEMD. This paper involves an overview of these standard algorithms, and also provides a focus on their limitations against common attacks. These study shows that these standard hash function algorithms suffer collision attacks and time inefficiency. Other types of hash functions are also highlighted in comparison with the standard hash function algorithm in performing the resistance against common attacks. It shows that these algorithms are still weak to resist against collision attacks

Gradual color clustering elimination for outdoor image segmentation

One of the color reduction methods is color clustering, which has been applied for segmentation. Nonetheless, it has not been an appropriate method due to the automatically images change by luminance effects and color/texture variety. Hence, it can be done by improving the usual color clustering methods called customizing segmentation methods. This study focuses on customizing the color clustering methods for segmentation and object recognition in the outdoor images by utilizing a multi - phase procedure through a multi - resolution platform, based on self - organizing neural network, call ed gradual color Cluster Elimination (GCCE). The proposed method has been evaluated on outdoor images dataset namely BSDS and the results have been compared to PRI, NPR, and GCE statistical metrics of the latest segmentation methods which demonstrated that the proposed method has a satisfactory performance for the segmentation of the outdoor scenes

Mobile Business Intelligence Acceptance Model for Organisational Decision Making

Mobile Business Intelligence (BI) is the ability to access BI-related data such as key performance indicators (KPIs), business metric and dashboard through mobile device. Mobile BI addresses the use-case of remote or mobile workers that need on-demand access to business-critical data. User acceptance on mobile BI is an essential in order to identify which factors influence the user acceptance of mobile BI application. Research on mobile BI acceptance model on organizational decision-making is limited due to the novelty of mobile BI as newly emerged innovation. In order to answer gap of the adoption of mobile BI in organizational decision-making, this paper reviews the existing works on mobile BI Acceptance Model for organizational decision-making. Two user acceptance models which are Technology Acceptance Model and Technology Acceptance Model for Mobile Services will be review. Realizing the essential of strategic organizational decision-making in determining success of organizations, the potential of mobile BI in decision-making need to be explore. Since mobile BI still in its infancy, there is a need to study user acceptance and usage behavior on mobile BI in organizational decision-making. There is still opportunity for further investigate the impact of mobile BI on organizational decision-making

A review and open issues of diverse text watermarking techniques in spatial domain

Nowadays, information hiding is becoming a helpful technique and fetches more attention due to the fast growth of using the internet; it is applied for sending secret information by using different techniques. Watermarking is one of major important technique in information hiding. Watermarking is of hiding secret data into a carrier media to provide the privacy and integrity of information so that no one can recognize and detect it's accepted the sender and receiver. In watermarking, many various carrier formats can be used such as an image, video, audio, and text. The text is most popular used as a carrier files due to its frequency on the internet. There are many techniques variables for the text watermarking; each one has its own robust and susceptible points. In this study, we conducted a review of text watermarking in the spatial domain to explore the term text watermarking by reviewing, collecting, synthesizing and analyze the challenges of different studies which related to this area published from 2013 to 2018. The aims of this paper are to provide an overview of text watermarking and comparison between approved studies as discussed according to the Arabic text characters, payload capacity, Imperceptibility, authentication, and embedding technique to open important research issues in the future work to obtain a robust method

Predictive visual analytics for machine learning model in house price prediction: a case study

As an individual, buying a house is a nerve-racking process. It requires a huge amount of money, time-consuming and relentless worry whether it is a good deal or not. The uncertainty in the housing market and the motivation to own a house have raised questions among homeowners and buyers regarding how accurate the house prices can be predicted, and what attributes or factors influenced the house prices. There were studies conducted in Malaysia that applied machine learning in predicting house prices. However, most of the studies using the Valuation and Property Service Department (VPSD) dataset were conducted in different states, namely Selangor, Kuala Lumpur, and Johor. Thus, there is an opportunity to extend the study to predict the house price in Penang state, Malaysia due to the increase in house prices in Penang is the highest among all the states in Malaysia. Therefore, this study aims to produce a machine learning predictive model using 2,666 terrace houses actual property transactions in Penang from VPSD from January 2018 until December 2019. The dataset is split into a train-test (estimation-validation) set with 80% train set and 20% test set (80:20) proportion and separated by two groups of different feature selection dataset which is all feature and selected features. Hence, to capture the different performances from both groups. The predictive model development using Multiple Linear Regression, Random Forest, and K-Nearest Neighbors algorithms with different parameters. The predictive model's performance was evaluated based on error measurement metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Its reveals that Random Forest of 250 trees using all feature has been chosen as the best model among others which produces 23,786.856 for Root Mean Square Error (RMSE), 13,769.965 for Mean Absolute Error (MAE), and 4.674% Mean Absolute Percentage Error (MAPE) from the train set