Massudi Mahmuddin

A weighted dominant color descriptor for content-based image retrieval

Color has been extensively used in the process of image retrieval. The dominant color descriptor (DCD) that was proposed by MPEG-7 is a famous case in point. It is based on compactly describing the prominent colors of an image or a region. However, this technique suffers from some shortcomings; especially with respect to object-based image retrieval. In this paper, a new semantic feature extracted from dominant colors (weight for each DC) is proposed. The newly proposed technique helps reduce the effect of image background on image matching decision where an objects colors receive much more focus. In addition, a modification to DC-based similarity measure is also proposed. Experimental results demonstrate that the proposed descriptor with the similarity measure modification performs better than the existing descriptor in content-based image retrieval application. The proposed descriptor considers as step forward to the object-based image retrieval.

Ensemble classifier for epileptic seizure detection for imperfect EEG data

Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR = 1 dB, 84% when SNR = 5 dB, and 88% when SNR = 10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.

Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring

Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring. The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression. To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechies wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification. Extensive experimental work is conducted, utilizing four classification models. The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value. The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the applications accuracy.

Interference issues and mitigation method in WSN 2.4GHz ISM band: A survey

Current lifestyles promote the development and advancement in wireless technologies, especially in Wireless Sensor Networks (WSN) due to its several benefits. WSN offers a low cost, low data rate, flexible routing, longer lifetime, and low-energy consumption suitable for unmanned and long term monitoring. Among huge WSN applications, some key applications are smart houses, environmental monitoring, military applications, and other monitoring applications. As a result, ubiquitous increase in the number of wireless devices occupying the 2.4GHz frequency band. This causes a dense wireless connection followed by interference problem to WSN in the 2.4GHz frequency band. WSN is most affected by the interference issue because it has a lower data rate and transmission power compared to WLAN. Despite efforts made by researchers, to the authors knowledge, the interference issue is still a major problem in wireless networks. This paper aims to review the coexistence and interference issues of existing wireless technologies in the 2.4GHz Industrial, Scientific and Medical (ISM) band.

PROTEIN TERTIARY STRUCTURE PREDICTION BASED ON MAIN CHAIN ANGLE USING A HYBRID BEES COLONY OPTIMIZATION ALGORITHM

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

DCBRP: a deterministic chain-based routing protocol for wireless sensor networks

Background Wireless sensor networks (WSNs) are a promising area for both researchers and industry because of their various applications The sensor node expends the majority of its energy on communication with other nodes. Therefore, the routing protocol plays an important role in delivering network data while minimizing energy consumption as much as possible. The chain-based routing approach is superior to other approaches. However, chain-based routing protocols still expend substantial energy in the Chain Head (CH) node. In addition, these protocols also have the bottleneck issues.Methods A novel routing protocol which is Deterministic Chain-Based Routing Protocol (DCBRP). DCBRP consists of three mechanisms: Backbone Construction Mechanism, Chain Head Selection (CHS), and the Next Hop Connection Mechanism. The CHS mechanism is presented in detail, and it is evaluated through comparison with the CCM and TSCP using an ns-3 simulator.ResultsIt show that DCBRP outperforms both CCM and TSCP in terms of end-to-end delay by 19.3 and 65%, respectively, CH energy consumption by 18.3 and 23.0%, respectively, overall energy consumption by 23.7 and 31.4%, respectively, network lifetime by 22 and 38%, respectively, and the energy*delay metric by 44.85 and 77.54%, respectively.ConclusionDCBRP can be used in any deterministic node deployment applications, such as smart cities or smart agriculture, to reduce energy depletion and prolong the lifetimes of WSNs.

An Efficient Perceptual Color Indexing Method for Content-Based Image Retrieval Using Uniform Color Space

Dominant Color Descriptor (DCD) is one of the famous descriptors in Content-based image retrieval (CBIR). Sequential search is one of the common drawbacks of most color descriptors especially in large databases. In this paper, dominant colors of an image are indexed to avoid sequential search in the database where uniform RGB color space is used to index images in LUV perceptual color space. Proposed indexing method will speed up the retrieval process where the dominant colors in query image are used to reduce the search space. Additionally, the accuracy of color descriptors is improved due to this space reduction. Experimental results show effectiveness of the proposed color indexing method in reducing search space to less than 25 % without degradation the accuracy.

A conceptual model of mobile commerce acceptance in collectivist cultures

The paper aims to develop a model that can explain the critical determinants that influencing the behavioral intention (BI) of mobile commerce (m-commerce) in collectivist cultures such as the Arab culture; particularly Jordan. The proposed model employs the technology acceptance model (TAM). TAM has been augmented with subjective norms (SN) from the Theory of Reasoned Action (TRA). The augmented model further decomposed SN into different levels. In addition, three variables were used to extended TAM: Personal innovativeness in IT (PIIT) from Diffusion of Innovations (DOI), Facilitating Conditions (FC) from Decomposed Theory of Planned Behavior (DTPB) and cost derived from literature. M-commerce acceptance literature revealed the majority of the researches were conducted in developed nations. Moreover, the academic research of m-commerce acceptance in the Arab world is limited; therefore, there has been little attempt to fill the gap in understanding the antecedents of BI to accept m-commerce in the region. This conceptual model will provide a foundation for future research and a greater knowledge of the potential users of m-commerce adoption in Jordan.

A proposed method optimizing energy usage for software process

Nowadays there are several ways that calculate the usage of carbon produce in plantations or big firms. These calculators contribute to different scopes of productions. However, the computer processes also create the amount of carbon dioxide(C02) usage. This issue can be overcome with a specific calculator that provides services to estimate the amount of carbon usage. Therefore, this paper proposes on solving the issue of carbon usage for computer processes through the use of C02 calculator.

Walsh transform with moving average filtering for data compression in wireless sensor networks

Due to the peculiarity of wireless sensor networks (WSNs), where a group of sensors continuously transmit data to other sensors or to the fusion center, it is crucial to compress the transmitted data in order to save the consumed power, which is paramount in the case of portable devices. There exists several techniques for data compression such as discrete wavelet transform (DWT) based, which fails to achieve high compression ratio for an acceptable distortion ratio. In this paper, we explore exploiting Walsh transform with a moving average filtering (MAF) for data compression in WSNs. One application of WSN is wireless body sensor networks. We apply Walsh transform on real Electroencephalogram (EEG) data collected from patients. Furthermore, we compare our results to DWT and show the superiority of exploiting Walsh transform for data compression. We show that using MAF with Walsh transform enhances the compression ratio for up to 30% more than that achieved by DWT.