Adaptive Group Testing on Networks with Community Structure: The Stochastic Block Model

Group testing was conceived during World War II to identify soldiers infected with syphilis using as few tests as possible, and it has attracted renewed interest during the COVID-19 pandemic. A long-standing assumption in the probabilistic variant of the group testing problem is that individuals are infected by the disease independently. However, this assumption rarely holds in practice, as diseases often spread through interactions between individuals and therefore cause infections to be correlated. Inspired by characteristics of COVID-19 and other infectious diseases, we introduce an infection model over networks which generalizes the traditional i.i.d. model from probabilistic group testing. Under this model, we ask whether knowledge of the network structure can be leveraged to perform group testing more efficiently, focusing specifically on community-structured graphs drawn from the stochastic block model. We prove that a simple community-aware algorithm outperforms the baseline binary splitting algorithm when the model parameters are conducive to “strong community structure.”Moreover, our novel lower bounds imply that the community-aware algorithm is order-optimal in certain parameter regimes. We extend our bounds to the noisy setting and support our results with numerical experiments. IEEE

Evaluation of Using Genetic Algorithm and ArcGIS for Determining the Optimal-Time Path in the Optimization of Vehicle Routing Applications

Transportation is regarded as one of the most important issues currently being researched;this issue needs the search for approaches or processes that might lessen many contemporary traffic concerns. Congestion, pollution, and accidents have escalated lately, negatively impacting urban environments, economic development, and citizens’ lifestyles. The rise of illnesses and epidemics throughout the world, such as COVID-19, has created an urgent need to find the best way to save people’s lives. The vehicle routing problem (VRP) is a well-known moniker for improving transportation systems and is regarded as one of the ancient and contemporary difficulties in route planning applications. One of the main tasks of VRP is serving many customers by determining the optimal route from an initial point to a destination on a real-time road map. The best route is not necessarily the shortest-distance route, but, in emergency cases, it is the route that takes the least fitness cost (time) and the fastest way to arrive. This paper aims to provide an adaptive genetic algorithm (GA) to determine the optimal time route, taking into account the factors that influence the vehicle arrival time and cause delays. In addition, the Network Analyst tool in ArcGIS is used to determine the optimal route using real-time map based on the user’s preferences and suggest the best one. Experimental results indicate that the performance of GA is mainly determined by an efficient representation, evaluation of fitness function, and other factors such as population size and selection method.

Improved Glowworm Swarm Optimization Algorithm Based on a Sigmoid Function for the Absolute Value Equation

Solving the absolute value equation (AVE) is a nondifferentiable NP-hard and continuous optimization problem with a wide range of applications. Because its solutions have different forms, it is challenging to design the most efficient algorithm that can solve different AVEs without using overcomplicated technical improvement and problem-dependent objectives. Hence, this paper proposed an improved glowworm swarm optimization (GSO) algorithm with an adaptive step size strategy based on the sigmoid function (SIGGSO) that solves the AVEs. Seven test AVEs, including multisolution and high-dimensional AVEs, are selected for testing and compared with seven metaheuristic algorithms. The experimental results show that the proposed SIGGSO algorithm has higher solution accuracy and stability when seeking multiple solution of AVEs compared to the basic GSO. Moreover, it obtains competitive advantages on multisolution and high-dimensional AVEs compared with other metaheuristic algorithms and provides an effective method for engineering and scientific calculations.

A Novel Facial Mask Detection Using Fast-YOLO Algorithm

The extremely high transmission rate of the COVID-19 has made the supply of medical resources in countries around the world in short supply. The implementation of quarantine in order to avoid group infections has a serious impact on the economy, transportation, education and other aspects. Epidemic prevention will be a routine task that needs to be carried out for a long time and cannot be neglected. In view of the fact that wearing masks is currently an effective method of epidemic prevention, and the current face detection models are not effective for masked faces, and pedestrians who have not worn masks in the correct way. It may spread the epidemic. This research will establish a face data set with three kinds of annotations, and combine a variety of deep learning convolutional neural network architectures and methods to design a face detection model that can quickly train and detect wearing a mask, not wearing a mask, and wearing a mask incorrectly faces. In the hope of contributing to the epidemic prevention, we use an adaptive algorithm to adjust the image size to reduce unnecessary operations, and modify the CIOU_LOSS error function to speed up the operation. Experiments have confirmed that our algorithm saves 70% of the time compared to YOLO v5m with the same accuracy. © 2022 IEEE.

Optimizing the Sensitivity of Biological Particle Detectors through Atmospheric Particle Analysis According to Climatic Characteristics in South Korea

Biological agents used in biological warfare or bioterrorism are also present in bioaerosols. Prompt identification of a biological weapon and its characteristics is necessary. Herein, we optimized an environmentally adaptive detection algorithm that can better reflect changes in the complex South Korean environment than the current models. The algorithm distinguished between normal and biological particles using a laser-induced fluorescence-based biological particle detector capable of real-time measurements and size classification. We ensured that the algorithm operated with minimal false alarms in any environment by training based on experimental data acquired from an area where rainfall, snow, fog and mist, Asian dust, and water waves on the beach occur. To prevent time and money wastage due to false alarms, the detection performance for each level of sensitivity was examined to enable the selection of multiple sensitivities according to the background, and the appropriate level of sensitivity for the climate was determined. The basic sensitivity was set more conservatively than before, with a 3% alarm rate at 20 agent-containing particles per liter of air (ACPLA) and a 100% alarm rate at 63 ACPLA. The reliability was increased by optimizing five variables. False alarms did not occur in situations where no alarm was unnecessary.

An Adaptive algorithm to predict bad debts during COVID-19 Pandemic

Loan recovery during the COVID-19 pandemic is anxious. Automated decision-making would boost the identification of bad debts while issuing loans. The objective of the proposed work is thus to design and implement an adaptive algorithm, which will be used to predict bad debts. Machine learning is an artificial intelligence technology, which gives systems the ability to automatically learn and improve from experience without explicit programming. The adaptive algorithm proposed is deterministic, uses two parameters known as neighborhood distance and minimum support threshold value for the risk profile, and can be very useful in predicting bad debts. It produces overlapped as well as non-overlapped clusters. This algorithm can detect the outliers with the help of an adaptive threshold value for the object's risk profile attribute. Objects with a moderately high or high value of risk profile attribute may emerge as outliers, and these outliers can be known as bad debts. The clusters generated are labeled as paid fully, not paid fully, and not paid. It can also generate clusters of different sizes. The proposed adaptive deterministic algorithm clusters the dataset without knowing the number of clusters. Many clusters are generated using this algorithm, but the parameter risk profile minimum threshold value prunes the clusters being formed. This proposed adaptive algorithm is testedusing real and artificial data sets and shows 83% accuracy in bad debt prediction. © 2022 IEEE

INFORMATION RELIABILITY AND SECURITY INCREASE IN ECONOMIC AND HEALTH SYSTEMS USING ADAPTIVE TRANSMISSION ALGORITHMS

The pandemic of COVID 19 virus has caused a lot of damage around the world. In addition to human lives, the economies of entire countries are at risk. In order to avoid business, health and education systems breakdown, it was necessary to find a new models of their functioning. An increasing number of these systems depends on IT support, so online teaching, obtaining information on health status and video conferencing meetings in business corporations has become a daily life. Although from the point of view of the end users of these systems, the result is information or data that has been processed, special attention should be highlighted to the transfer of information of various types through existing modern communication systems. In case that information of a great importance is intercepted or unreliable, the consequences can be catastrophic. Given the current situation at the global level and the importance of the information transmitted, in this paper we will focus on the protection and reliability of information transmission using adaptive transmission algorithms. Modeling and implementation of adaptive transmission algorithms can allow us to adapt the data rate with higher bandwidth or fixed data rate with lower bandwidth depending on the needs of end users and their requirements. Whether optical cables, radio frequency systems or modern wireless optical communication systems are used for the transmission of information, adaptive transmission algorithms can be successfully implemented and thus greater security and reliability of the transmitted information can be achieved. In addition to the model of adaptive transmission algorithms, the pseudocode of their functioning will be given in this paper. Finally, a comparative analysis of these algorithms observed through a measure of channel capacity will be graphically presented.

Distributed clustering algorithm for adaptive pandemic control

The COVID-19 pandemic has had severe consequences on the global economy, mainly due to indiscriminate geographical lockdowns. Moreover, the digital tracking tools developed to survey the spread of the virus have generated serious privacy concerns. In this paper, we present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria. The algorithm is fully distributed and therefore, individuals do not know any information about the group they belong to. Thus, we present a distributed clustering algorithm for adaptive pandemic control. Author

Design, Development, and Testing of an Intelligent Wearable Robotic Exoskeleton Prototype for Upper Limb Rehabilitation.

Neuromotor rehabilitation and recovery of upper limb functions are essential to improve the life quality of patients who have suffered injuries or have pathological sequels, where it is desirable to enhance the development of activities of daily living (ADLs). Modern approaches such as robotic-assisted rehabilitation provide decisive factors for effective motor recovery, such as objective assessment of the progress of the patient and the potential for the implementation of personalized training plans. This paper focuses on the design, development, and preliminary testing of a wearable robotic exoskeleton prototype with autonomous Artificial Intelligence-based control, processing, and safety algorithms that are fully embedded in the device. The proposed exoskeleton is a 1-DoF system that allows flexion-extension at the elbow joint, where the chosen materials render it compact. Different operation modes are supported by a hierarchical control strategy, allowing operation in autonomous mode, remote control mode, or in a leader-follower mode. Laboratory tests validate the proper operation of the integrated technologies, highlighting a low latency and reasonable accuracy. The experimental result shows that the device can be suitable for use in providing support for diagnostic and rehabilitation processes of neuromotor functions, although optimizations and rigorous clinical validation are required beforehand.