scholarly journals A Multi-Objective Decision-Based Solution (MODM) for Facility Location-Allocation Problem Using Cuckoo Search and Genetic Algorithms

2020 ◽  
Author(s):  
Amir Shimi ◽  
Mohammad Reza Ebrahimi Dishabi ◽  
Mohammad Abdollahi Azgomi
Keyword(s):  
Genetic Algorithms ◽  
Performance Metrics ◽  
Road Traffic ◽  
Search Algorithm ◽  
Autonomous Vehicle ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To automatic parking, controlling steer angle, gas hatch, and brakes need to be learned. Due to the increase in the number of cars and road traffic, car parking space has decreased. Its main reason is information error. Because the driver does not receive the necessary information or receives it too late, he cannot take appropriate action against it. This paper uses two phases: the first phase, for goal coordination, was used genetic algorithms and the Cuckoo search algorithm was used to increase driver information from the surroundings. Using the Cuckoo search algorithm and considering the limitations, it increases the driver’s level of information from the environment. Also, by exchanging information through the application, it enables the information to reach the driver much more quickly and the driver reacts appropriately at the right time. The suggested protocol is called the MODM-based solution. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the parking system performance metrics are enhanced based on the detection rate, false-negative rate, and false-positive rate.

scholarly journals Registration Status Prediction of Students using Machine Learning in the Context of Private University of Bangladesh

2019 ◽  
Vol 9 (1) ◽  
pp. 2594-2600 ◽  
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forest ◽  
Performance Metrics ◽  
False Positive Rate ◽  
False Negative ◽  
Private Universities ◽  
False Negative Rate ◽  
Private University ◽  
Data Set

Bangladesh is a densely populated country where a large portion of citizens is living under poverty. In Bangladesh, a significant portion of higher education is accomplished at private universities. In this twenty-first century, these students of higher education are highly mobile and different from earlier generations. Thus, retaining existing students has become a great challenge for many private universities in Bangladesh. Early prediction of the total number of registered students in a semester can help in this regard. This can have a direct impact on a private university in terms of budget, marketing strategy, and sustainability. In this paper, we have predicted the number of registered students in a semester in the context of a private university by following several machine learning approaches. We have applied seven prominent classifiers, namely SVM, Naive Bayes, Logistic, JRip, J48, Multilayer Perceptron, and Random Forest on a data set of more than a thousand students of a private university in Bangladesh, where each record contains five attributes. First, all data are preprocessed. Then preprocessed data are separated into the training and testing set. Then, all these classifiers are trained and tested. Since a suitable classifier is required to solve the problem, the performances of all seven classifiers need to be thoroughly assessed. So, we have computed six performance metrics, i.e. accuracy, sensitivity, specificity, precision, false positive rate (FPR) and false negative rate (FNR) for each of the seven classifiers and compare them. We have found that SVM outperforms all other classifiers achieving 85.76% accuracy, whereas Random Forest achieved the lowest accuracy which is 79.65%.

scholarly journals A Smart Agent-Based Technique Using Hash Function to Protect Communication within UAVs in Unmanned Aerial Systems

2020 ◽  
Author(s):  
Maryam Faraji
Keyword(s):  
Black Hole ◽  
Network Performance ◽  
Performance Metrics ◽  
Defense Mechanism ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Unmanned Aerial Systems ◽  
Agent Based ◽  
Aerial Systems

Unmanned aerial systems (UASs) create an extensive fighting capability of the developed military forces. Particularly, these systems carrying confidential data are exposed to security attacks. By the wireless’s nature within these networks, they become susceptible to different kinds of attacks, hence, it seems essential to design the appropriate safety mechanism in such networks. The sinkhole attack is one of the most dangerous and threatening attacks amongst types of attack in UAS. A malicious UAV exists in such a threat attacking as a black hole for absorbing all traffic in the network. Mainly, in a Flow-based protocol, the attacker considers the requests on the route, then, it replies to the target UAV such as high quality or the best route towards Gard station. The malicious UAV is able to only insert itself on one occasion between the nodes relating to each other (such as sink node and sensor node), and act for passing packets among them. In this study, the malicious attacks are detected and purged using two stages were. In the first stage, some principles and rules are used to detect black hole, gray hole, and sinkhole attacks. In the second stage, using a smart agent-based strategy negotiation procedure for three steps, a defense mechanism is designed to prevent these attacks. The smart agent is used by reliable neighbors via the negotiation procedure for three steps, hence, the traffic formed by the malicious UAV is not considered. The suggested protocol is called SAUAS. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the UAS network performance metrics are enhanced based on the packet delivery rate, detection rate, false-negative rate and false-positive rate.

scholarly journals Secure Communication between UAVs Using a Method Based on Smart Agents in Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Maryam Faraji ◽  
Reza Fotohi
Keyword(s):  
Black Hole ◽  
Secure Communication ◽  
Network Performance ◽  
Performance Metrics ◽  
Defense Mechanism ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Unmanned Aerial Systems ◽  
Positive Rate

Unmanned aerial systems (UASs) create an extensive fighting capability of the developed military forces. Particularly, these systems carrying confidential data are exposed to security attacks. By the wireless’s nature within these networks, they become susceptible to different kinds of attacks, hence, it seems essential to design the appropriate safety mechanism in such networks. The sinkhole attack is one of the most dangerous and threatening attacks amongst types of attack in UAS. A malicious UAV exists in such a threat attacking as a black hole for absorbing all traffic in the network. Mainly, in a Flow-based protocol, the attacker considers the requests on the route, then, it replies to the target UAV such as high quality or the best route towards Gard station. The malicious UAV is able to only insert itself on one occasion between the nodes relating to each other (such as sink node and sensor node), and act for passing packets among them. In this study, the malicious attacks are detected and purged using two stages were. In the first stage, some principles and rules are used to detect black hole, gray hole, and sinkhole attacks. In the second stage, using a smart agent-based strategy negotiation procedure for three steps, a defense mechanism is designed to prevent these attacks. The smart agent is used by reliable neighbors via the negotiation procedure for three steps, hence, the traffic formed by the malicious UAV is not considered. The suggested protocol is called SAUAS. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the UAS network performance metrics are enhanced based on the packet delivery rate, detection rate, false-negative rate and false-positive rate.

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

2020 ◽  
pp. 1748006X2094653
Author(s):  
Behzad Karimi ◽  
Seyed Taghi Akhavan Niaki ◽  
Amir Hossein Niknamfar ◽  
Mahsa Gareh Hassanlu
Keyword(s):  
Manufacturing Systems ◽  
Production Efficiency ◽  
Performance Metrics ◽  
Job Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Automated Guided Vehicles ◽  
Job Shops ◽  
Multi Objective

The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines in each shop are limited to failures based on either an Exponential or a Weibull distribution via a constant rate, the machines in different shops fail based on different failure rates. Meanwhile, as the model does not contain any closed-form equation to measure the machine reliability in the case of Weibull failure, a simulation approach is employed to estimate the shop reliability to be further maximized using the proposed model. Besides, the automated guided vehicles are restricted to failures according to an Exponential distribution. Furthermore, choosing the best locations of the shops is proposed among some potential places. The proposed NP-Hard problem is then solved by designing a novel non-dominated sorting cuckoo search algorithm. Furthermore, a multi-objective teaching-learning-based optimization, as well as a multi-objective invasive weed optimization are designed to validate the results obtained. Ultimately, a novel AHP-TOPSIS method is carried out to rank the algorithms in terms of six performance metrics.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Comparative study of ultrasonographic findings with the operative findings of biliary surgery

2020 ◽  
Vol 22 (1) ◽  
pp. 25-29
Author(s):  
Zubayer Ahmad ◽  
Mohammad Ali ◽  
Kazi lsrat Jahan ◽  
ABM Khurshid Alam ◽  
G M Morshed
Keyword(s):  
Bile Duct ◽  
Common Bile Duct ◽  
Imaging Modality ◽  
False Positive Rate ◽  
Gallstone Disease ◽  
False Negative ◽  
False Negative Rate ◽  
Biliary Disease ◽  
Biliary Surgery ◽  
Operative Findings

Background: Biliary disease is one of the most common surgical problems encountered all over the world. Ultrasound is widely accepted for the diagnosis of biliary system disease. However, it is a highly operator dependent imaging modality and its diagnostic success is also influenced by the situation, such as non-fasting, obesity, intestinal gas. Objective: To compare the ultrasonographic findings with the peroperative findings in biliary surgery. Methods: This prospective study was conducted in General Hospital, comilla between the periods of July 2006 to June 2008 among 300 patients with biliary diseases for which operative treatment is planned. Comparison between sonographic findings with operative findings was performed. Results: Right hypochondriac pain and jaundice were two significant symptoms (93% and 15%). Right hypochondriac tenderness, jaundice and palpable gallbladder were most valuable physical findings (respectively, 40%, 15% and 5%). Out of 252 ultrasonically positive gallbladder, stone were confirmed in 249 cases preoperatively. Sensitivity of USG in diagnosis of gallstone disease was 100%. There was, however, 25% false positive rate detection. Specificity was, however, 75% in this case. USG could demonstrate stone in common bile duct in only 12 out of 30 cases. Sensitivity of the test in diagnosing common bile duct stone was 40%, false negative rate 60%. In the series, ultrasonography sensitivity was 100% in diagnosing stone in cystic duct. USG could detect with relatively good but less sensitivity the presence of chronic cholecystitis (92.3%) and worm inside gallbladder (50%). Conclusion: Ultrasonography is the most important investigation in the diagnosis of biliary disease and a useful test for patients undergoing operative management for planning and anticipating technical difficulties. Journal of Surgical Sciences (2018) Vol. 22 (1): 25-29

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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