Multi-commodity flow problem concerns with the transshipment of more than one commodities from respective sources to the corresponding sinks without violating the capacity constraints on the arcs. If the objective of the problem is to sent maximum amount of flow within given time horizon, then it becomes the maximum flow problem. In multi-commodity flow problem, flow of different commodities departing from their sources may arrive the common intermediate node at the same time and have to share the capacity through the arc. The sharing of the capacity in the common arc (bundle arc) is one of the major issues in multi-commodity flow problem. To deal with this problem, we propose the proportional capacity sharing and flow-dependent capacity sharing techniques.

If the sharing of the capacity of the bundle arc is set in proportion to the bottleneck capacity of incoming arcs of each commodities from their respective sources, then it is known as proportional capacity sharing. In this case, share of the capacity of bundle arc for each commodity is fixed and the multi-commodity flow problem is reduced to independent single commodity flow problems. To avoid the fractional flow, we can use ceiling and floor functions with appropriate manner. Similarly, if the sharing of the capacity of bundle arc is made according to the inflow rate of the flow of each commodity, then it is termed as flow-dependent capacity sharing. In this method, the share of capacity of bundle arc may not always be same as the flow may vary over time.


In this work, we present the maximum static as well as maximum dynamic multi-commodity flow problems with proportional as well as flow-dependent capacity sharings. We also present polynomial and pseudo-polynomial time algorithms for these problems according to the proportional and flow-dependent sharing techniques, respectively.

As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper presents a survey of deep learning for lung disease detection in chest X ray images. The taxonomy presents five attributes that are common in the surveyed articles: types of deep learning algorithms, features used for detection of abnormalities, data augmentation, transfer learning, and types of lung diseases. The presented taxonomy could prove to be extremely useful for other researchers to ideate their research contributions in this area. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

This study presents a coalition-based parallel metaheuristic algorithm for solving Permutation Flow Shop Scheduling Problems (PFSP). The proposed approach incorporates five different single-solution based metaheuristic algorithms (SSBMA) (Simulated Annealing Algorithm, Random Search Algorithm, Great Deluge Algorithm, Threshold Accepting Algorithm and Greedy Search Algorithm) and a recently proposed swarm intelligence based metaheuristic algorithm that is known as Weighted Superposition Attraction Algorithm (WSA). While SSBMAs are responsible for exploring the search space, WSA serves as a controller that handles the coalition process. SSBMAs perform their search simultaneously by utilizing MATLAB’s parallel programming tool. When all SSBMAs complete their search, they share their findings with other SSBMAs through the superposition mechanism of WSA. As a result of sharing, SSBMAs move towards the superposition point found by the coalition of SSBMAS or they do their own local search if they are in a better position (has a better fitness) than the superposition. Before a new parallel search process, SSBMAs determine their new characteristics (new parameters). This search and coalition process last until a predetermined iteration number is reached. The proposed approach is tested on many PFSP benchmarks and results are compared against the state of the art algorithms from the literature. Moreover, the proposed algorithm is also tested against its constituents (SSBMAS and WSA) and its serial version. Non-parametric statistical tests are performed to compare the performance of the proposed approach statistically with the state of the art algorithms, its constituents and its serial version. The statistical results prove the effectiveness of the proposed approach.

This study aims at constructing new and effective fully explicit numerical schemes for solving the heat conduction equation. We use fractional time steps for the odd cells in the well-known odd-even hopscotch structure and fill it with several different formulas to obtain a large number of algorithm-combinations. We generate random parameters in a highly inhomogeneous spatial distribution to set up discretized systems with various stiffness ratios and systematically test these new methods by solving these systems. The best combinations are verified by comparing them to analytical solutions. We also show analytically that their rate of convergence is two and that they are unconditionally stable.

AI-based Arabic text recognition is a process to simultaneously classify the different contextual Arabic contents into a proper category for easy understanding. With the increasing number of Arabic texts in our social life, traditional machine learning approaches are facing different challenges due to the complexity of morphology and the variation delicate of the Arabic language. In this study, a novel deep learning Arabic text computer-aided recognition (ArCAR) is proposed to represent and recognize Arabic text at the character level based on the capability of a deep convolutional neural network (CNN). The proposed ArCAR system is validated using five-fold cross-validation tests for two applications: (1) Arabic text document classification and (2) Arabic sentiment analysis. For document classification, we use nine different datasets in the multiclass problem, while four different datasets are used to evaluate our proposed system for Arabic sentiment analysis. The ArCAR system shows its capability for character-level Arabic text recognition for both applications. For document classification, the ArCAR system achieves the best performance using the Alarabiya-balance dataset in terms of overall accuracy, recall, precision, and F1-score, by 97.76%, 94.08%, 94.16%, and 94.09%, respectively. Meanwhile, the ArCAR performs well for Arabic sentiment analysis, achieving the best performance using the HARD-balance dataset in terms of overall accuracy and F1-score, by 93.58% and 93.23%, respectively. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, understanding, and classifications system.

Multi-commodity flow problems concern with the transshipment of more than one commodities from respective sources to the corresponding sinks without violating the capacity constraints on the arcs. If the objective of the problem is to send the maximum amount of flow within given time horizon, then it becomes the maximum flow problem. In multi-commodity flow problem, flow of different commodities departing from their sources arrive at the common intermediate node and have to share the capacity through the arc. The sharing of the capacity in the common arc (bundle arc) is one of the major issues in the multi-commodity flow problems. In this paper, we introduce the maximum static and maximum dynamic multi-commodity flow problems with proportional capacity sharing and present polynomial time algorithms to solve the problems. Similarly, we investigate the maximum dynamic multi-commodity flow problem with flow-dependent capacity sharing and present a pseudo-polynomial time solution strategy.

The measurement of Blood-Oxygen Saturation, Heart Rate, and Body Temperature are very prominent in monitoring patients. Photoplethysmography(PPG) is an optical method that can be used to measure Heart Rate, Blood-Oxygen Saturation, and many analytic about Cardiovascular Health of a patient by analyzing the waveform. With the COVID-19 pandemic, there is a high demand for a product that can remotely monitor such parameters of a COVID patient. This paper proposes two major design architectures for the product with optimized system implementations by utilizing the ESP32 development environment and cloud computing. In one method it discusses edge computing with the Fast Fourier Transform(FFT) algorithm to extract features from the waveform before transferring to the cloud while the other method transfers raw sensor values to the cloud without any loss of information. This paper especially compares the performance of both system architectures with respect to bandwidth, sampling frequency, and loss of information.

In a simple connected graph $G=(V,E)$, a subset of vertices $S \subseteq V$ is a dominating set in graph $G$ if any vertex $v \in V\setminus S$ is adjacent to some vertex $x$ from this subset. It is known that this problem is NP-hard, and hence there exists no exact polynomial-time algorithm that finds an optimal solution to the problem. This work aims to present an exact enumeration and heuristic algorithm that can be used for large-scale real-life instances. Our exact enumeration algorithm begins with specially derived lower and upper bounds on the number of vertices in an optimal solution and carries out a binary search within the successively derived time intervals. The proposed heuristic accomplishes a kind of depth-first search combined with breadth-first search in a solution tree. The performance of the proposed algorithms is far better than that of the state-of-the-art ones. For example, our exact algorithm has solved optimally problem instances with order 300 in 165 seconds. This is a drastic breakthrough compared to the earlier known exact method that took 11036 seconds for the same problem instance. On average, over all the 100 tested problem instances, our enumeration algorithm is 168 times faster.

Dielectric analysis (DEA) is a well-known technology for monitoring chemical processes e.g., the curing of adhesives. DEA compares the changes in phase and amplitude of a sinusoid applied to a specimen with its response signal. Multi-sine excitation signals give spectroscopic insight in fast chemical processes over bandwidths from 10¹ Hz to 10⁷ Hz. The crest factor (CF) determines the information density of a multi-sine signal. Minimizing the CF yields higher information density and is the goal of the presented work.

Four algorithms and a combination of two of them will be presented. The first one optimizes the phase angle of each signal thus reducing the CF. The second one optimizes the signal by calculating random phase angles and amplitudes. The combined algorithm alternates between the first and second optimization algorithm. Additionally, a simulated annealing approach and a genetic algorithm optimizing the CF were implemented. Optimizations were conducted for identical multi-sine configurations for each algorithm.

The results are compared with the performance of the algorithms Ojarand presented in his papers from 2017 [1] and 2014 [2]. First, multi-sine signals comprising the same configuration as used by Ojarand are optimized by the introduced algorithms. To overcome the limitations of the algorithms proposed by literature, the developed algorithms are applied to multi-sine signals comprising 50+ frequencies. The developed algorithms generally have better performance for optimization of multi-sine signals with wide bandwidth and non-flat amplitude spectra. The results are better in terms of resulting crest factor as well as computation time.

COVID-19 pandemic is a global health problem since December 2019. Up to date, the total number of confirmed, recovered and deaths has exponentially increased on daily basis worldwide. In this paper, a hybrid deep learning approach is proposed to directly classify the COVID-19 disease from both chest X-ray (CXR) and CT images. Two AI-based deep learning models, namely ResNet50 and EfficientNetB0, are adopted and trained using both chest X-ray and CT images. The public datasets consist of 7,863 and 2,613 chest X-ray and CT images are respectively used to deploy, train, and evaluate the proposed deep learning models. The deep learning model of EfficientNet always performed a better classification result achieving overall diagnosis accuracies of 99.36% and 99.23% using CXR and CT images, respectively. For the hybrid AI-based model, the overall classification accuracy of 99.58% is achieved. The proposed hybrid deep learning system seems to be trust worth and reliable for assisting health care systems, patients, and physicians.