A review of state-of-the-art techniques for abnormal human activity recognition

Chaos in fractional-order discrete neural networks with application to image encryption

Fuzzy based image edge detection algorithm for blood vessel detection in retinal images

(Good) Software documentation provides developers and users with a description of what a software system does, how it operates, and how it should be used. For example, technical documentation (e.g., an API reference guide) aids developers during evolution/maintenance activities, while a user manual explains how users are to interact with a system. Despite its intrinsic value, the creation and the maintenance of documentation is often neglected, negatively impacting its quality and usefulness, ultimately leading to a generally unfavourable take on documentation. Previous studies investigating documentation issues have been based on surveying developers, which naturally leads to a somewhat biased view of problems affecting documentation. We present a large scale empirical study, where we mined, analyzed, and categorized 878 documentation-related artifacts stemming from four different sources, namely mailing lists, Stack Overflow discussions, issue repositories, and pull requests. The result is a detailed taxonomy of documentation issues from which we infer a series of actionable proposals both for researchers and practitioners.

Software documentation issues unveiled

Machine learning : an artificial intelligence approach

During the last decades, information security has become a major issue. Encrypting and decrypting data have recently been widely investigated and developed because there is a demand for a stronger encryption and decryption which is very hard to crack. Cryptography plays major roles to fulfilment these demands. Nowadays, many of researchers have proposed many of encryption and decryption algorithms such as AES, DES, RSA, and others. But most of the proposed algorithms encounteredsome problems such as lack of robustness and significant amount of time added to packet delay to maintain the security on the communication channelbetween the terminals. In this paper, the security goals were enhanced via "A New Approach for Complex Encr ypting and Decrypting Data" which maintains the security on the communication channels by making it difficult for attacker to predicate a pattern as well as speed of the encryption / decryption scheme.

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A New Approach for Complex Encrypting and Decrypting Data

Over the past two decades, chaos-based encryption appeared as an original application for nonlinear dynamics and deterministic chaos to encrypt and decrypt data. In this paper, an implementation of digital image encryption scheme based on the Lorenz chaotic system is proposed. While in the process of generating chaotic key stream, the hash value of the plain image is embedded in the proposed cryptosystem to dynamically alter the initial secret keys to increase the security level. The proposed digital image encryption algorithm is described in detail along with its security analysis and implementation. The experimental results show that the proposed digital image encryption algorithm is efficient and has high security features and is suitable for practical uses across insecure networks.

Image encryption algorithm based on Lorenz chaotic map with dynamic secret keys

Diabetic retinopathy (i.e., DR), is an eye disorder caused by diabetes, diabetic retinopathy detection is an important task in retinal fundus images due the early detection and treatment can potentially reduce the risk of blindness. Retinal fundus images play an important role in diabetic retinopathy through disease diagnosis, disease recognition (i.e., by ophthalmologists), and treatment. The current state-of-the-art techniques are not satisfied with sensitivity and specificity. In fact, there are still other issues to be resolved in state-of-the-art techniques such as performances, accuracy, and easily identify the DR disease effectively. Therefore, this paper proposes an effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images that will satisfy the performance metrics (i.e., sensitivity, specificity, accuracy). The proposed automatic screening system for diabetic retinopathy was conducted in several steps: Pre-processing, optic disc detection and removal, blood vessel segmentation and removal, elimination of fovea, feature extraction (i.e., Micro-aneurysm, retinal hemorrhage, and exudates), feature selection and classification. Finally, a software-based simulation using MATLAB was performed using DIARETDB1 dataset and the obtained results are validated by comparing with expert ophthalmologists. The results of the conducted experiments showed an efficient and effective in sensitivity, specificity and accuracy.

An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e., human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval. Finally, a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.

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A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

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Obaida M. Al-Hazaimeh

Papers

A New Approach for Complex Encrypting and Decrypting Data

Image encryption algorithm based on Lorenz chaotic map with dynamic secret keys

An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

A Hybrid SVM NAÏVE-BAYES Classifier for Bright Lesions Recognition in Eye Fundus Images