Includes bibliographical references.

Article 1. MRF Model-based estimation of camera parameters and detection of underwater moving objects -- Article 2. Adaptive parameter estimation of IIR System-Based WSN using multihop diffusion in distributed approach -- Article 3. Scalable recommendation using large scale graph partitioning with pregel and giraph -- Article 4. Efficient regularization framework for histopathological image classification using convolutional neural networks -- Article 5. A multi-agent approach to segment arabic handwritten text lines -- Article 6. Classification of eyes based on fuzzy logic.

[Article Title: MRF Model-Based Estimation of Camera Parameters and Detection of Underwater Moving Objects / Susmita Panda and Pradipta Kumar Nanda, p. 1-29]

Abstract: The detection of underwater objects in a video is a challenging problem particularly when both the camera and the objects are in motion. In this article, this problem has been conceived as an incomplete data problem and hence the problem is formulated in expectation maximization (EM) framework. In the E-step, the frame labels are the maximum a posterior (MAP) estimates, which are obtained using simulated annealing (SA) and the iterated conditional mode (ICM) algorithm. In the M-step, the camera model parameters, both intrinsic and extrinsic, are estimated. In case of parameter estimation, the features are extracted at coarse and fine scale. In order to continuously detect the object in different video frames, EM algorithm is repeated for each frame. The performance of the proposed scheme has been compared with other algorithms and the proposed algorithm is found to outperform.

https://doi.org/10.4018/IJCINI.2020100101

[Article Title: Adaptive Parameter Estimation of IIR System-Based WSN Using Multihop Diffusion in Distributed Approach / Meera Dash, Trilochan Panigrahi, Renu Sharma, and Mihir Narayan Mohanty, p. 30-41]

Abstract: Distributed estimation of parameters in wireless sensor networks is taken into consideration to reduce the communication overhead of the network which makes the sensor system energy efficient. Most of the distributed approaches in literature, the sensor system is modeled with finite impulse response as it is inherently stable. Whereas in real time applications of WSN like target tracking, fast rerouting requires, infinite impulse response system (IIR) is used to model and that has been chosen in this work. It is assumed that every sensor node is equipped with IIR adaptive system. The diffusion least mean square (DLMS) algorithm is used to estimate the parameters of the IIR system where each node in the network cooperates themselves. In a sparse WSN, the performance of a DLMS algorithm reduces as the degree of the node decreases. In order to increase the estimation accuracy with a smaller number of iterations, the sensor node needs to share their information with more neighbors. This is feasible by communicating each node with multi-hop nodes instead of one-hop only.

https://doi.org/10.4018/IJCINI.2020100102

[Article Title: Scalable Recommendation Using Large Scale Graph Partitioning With Pregel and Giraph / Gourav Bathla, Himanshu Aggarwal, and Rinkle Rani, p. 43-61]

Abstract: Social Big Data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in social Big Data. If any user intends to select products such as movies, books, etc., from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity, but not efficient for scalability. The main focus of this article is to improve scalability in terms of locality and throughput and provides better recommendations to users with large-scale data in less response time. In this article, the social big graph is partitioned and distributed on different nodes based on Pregel and Giraph. In the proposed approach ScaleRec, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using proposed approach.

https://doi.org/10.4018/IJCINI.2020100103

[Article Title: Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks / Nassima Dif and Zakaria Elberrichi, p. 63-81]

Abstract: Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.

https://doi.org/10.4018/IJCINI.2020100104

[Article Title: A Multi-Agent Approach to Segment Arabic Handwritten Text Lines / Mohsine Elkhayati, Youssfi Elkettani, and Mohammed Mourchid, p. 82-100]

Abstract: In text line segmentation, there are three classes of methods: either by sorting physical units such as pixels or connected components (CC) constituting a line or by searching for the baseline of each word and grouping together those who participate in the same line. The third class analyzes the separation locations between the lines. After an overview of lines segmentation approaches, the authors introduced a new method emphasizing its simplicity, speed, and originality. The proposed approach detects the starting components of the lines in the first step. In the second step, it defines a number of agents that start the segmentation process from their starting points between the starting components of lines. Each agent aims to reach the left edge of the document through the correct path. The algorithm used by the agents is based on the morphological process, characteristics of the Arabic manuscript and a communication system. The experimental results on an Arabic dataset show that this approach is an effective solution for the segmentation of lines from different Arabic manuscripts.

https://doi.org/10.4018/IJCINI.2020100105

[Article Title: Classification of Eyes Based on Fuzzy Logic / Mohamed Fakir, Hatimi Hicham, Mohamed Chabi,and Muhammad Sarfraz, p. 101-112]

Abstract: The systems of eye classification in an image are indispensable in several domains. To better find the class of membership of the eye in a minimal time, the classic methods of detection are inadequate. Fuzzy logic is considered to be an effective technique for solving an eye classification problem. This article proposes a fuzzy approach for eye classification. The tasks of classification are realized in two steps. In the first step, the characteristic points of the image are extracted in order to locate the eye. These characteristic points allow generating a representative model of the eye. In the second step, the detected eyes have to pass by a fuzzy controller containing several parts: Fuzzification, inference rules, and defuzzification. Finally, the system gives the degree of membership of the detected eyes to each class in the database.

https://doi.org/10.4018/IJCINI.2020100106