Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage

We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The method exhibits a roughly linear runtime scaling over real-world networks ranging from 1000 to 100 million nodes. In a test on a social network with 1.8 billion edges, the algorithm finds the largest clique in about 20 minutes. Our method employs a branch and bound strategy with novel and aggressive pruning techniques. For instance, we use the core number of a vertex in combination with a good heuristic clique finder to efficiently remove the vast majority of the search space. In addition, we parallelize the exploration of the search tree. During the search, processes immediately communicate changes to upper and lower bounds on the size of maximum clique, which occasionally results in a super-linear speedup because vertices with large search spaces can be pruned by other processes. We apply the algorithm to two problems: to compute temporal strong components and to compress graphs.Comment: 11 page

Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems

Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic congestion and emission of greenhouse gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), particulate matter (PM), and oxides of nitrogen (NOx). Intelligent transport system (ITS) technologies can be implemented to lower pollutant emissions and reduction of fuel consumption. This paper investigates the ITS techniques and technologies for the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption and exhaust pollutant in the urban environment

Factors associated with relapse amongst substance abusers

Background: Relapse amongst substance abusers is common throughout the world, and Bangladesh is no exception to this. In Bangladesh, drug related problems are gradually becoming a burning issue in context of social, economical and medical perspective. The present study aimed to find out factors indicating relapse amongst substance abuser. Methods: This descriptive type of observational study was conducted in the combined military hospital and other government/private hospital/institute, especially the central drug addict treatment center, Dhaka. Informed consent was obtained prior to data collection. Collected data was classified, edited, coded, and entered into the computer for statistical analysis by using SPSS-23. The chi-Square test was used to analyze the categorical variables, and a p<0.05 was considered as statistically significant. Results: The study involved 100 patients who had a history of substance abuse. The most common substance abused was Yaba (27%), followed by cannabis (21%). The average duration of abuse for Yaba was 5.8 years, while the longest mean duration was for Alcohol (14.2 years). In the 2nd admission, the largest percentage of patients was aged 21-30 years and were male. The majority of patients were Muslim and were either unemployed or had a lower socioeconomic status. Patients age, occupation, socioeconomic status, peer pressure, and family problems all had a significant association (p<0.05) with relapse at different admissions. Peer pressure and family problems were also identified as factors affecting relapse, with 67.57% and 56.76% of patients experiencing them during their 2nd admission, respectively. Conclusions: The study found Yaba to be the most commonly used drug, followed by cannabis, phensedyl, heroin, etc. Alcohol was found to have the longest duration of abuse. Most patients were aged 21-30 and unemployed in multiple admissions. Peer pressure and unemployment were major factors in substance abuse, and psychiatric illness was a common factor in relapse. The results align with global findings and highlight the need for a comprehensive approach to addressing substance abuse, considering all relevant factors.

Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings

The focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energy-efficient and sustainable buildings, aiming for net-zero emissions. This research proposes a hybrid approach combining conventional solar panels with advanced solar window systems and building integrated photovoltaic (BIPV) systems. By analyzing the meteorological data and using the simulation models, we predict energy outputs for different cities such as Kuala Lumpur, Sydney, Toronto, Auckland, Cape Town, Riyadh, and Kuwait City. Although there are long payback times, our simulations demonstrate that the proposed all-PV blended system can meet the energy needs of modern buildings (up to 78%, location dependent) and can be scaled up for entire buildings. The simulated results indicate that Middle Eastern cities are particularly suitable for these hybrid systems, generating approximately 1.2 times more power compared to Toronto, Canada. Additionally, we predict the outcome of the possible incorporation of intelligent and automated systems to boost overall energy efficiency toward achieving a sustainable building environment

Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN

The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%

A faster RCNN based diabetic retinopathy detection method using fused features from retina images

Early identification of diabetic retinopathy (DR) is critical as it shows few symptoms at the primary stages due to the nature of its gradual and slow growth. DR must be detected at the early stage to receive appropriate treatment, which can prevent the condition from escalating to severe vision loss problems. The current study proposes an automatic and intelligent system to classify DR or normal condition from retina fundus images (FI). Firstly, the relevant FIs were pre-processed, followed by extracting discriminating features using histograms of oriented gradient (HOG), Shearlet transform, and Region-Based Convolutional Neural Network (RCNN) from FIs and merging them as one fused feature vector. By using the fused features, a machine learning (ML) based faster RCNN classifier was employed to identify the DR condition and DR lesions. An extended experiment was carried out by employing binary classification (normal and DR) from three publicly available datasets. With a testing accuracy of 98.58%, specificity of 97.12%, and sensitivity of 95.72%, this proposed faster RCNN deep learning technique with feature fusion ensured a satisfactory performance in identifying the DR compared to the relevant state-of-the-art works. By using a generalization validation strategy, this fusion-based method achieved a competitive performance with a detection accuracy of 95.75%