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.

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%.

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 Temporal dynamics of viral load and false negative rate influence the levels of testing necessary to combat COVID-19 spread

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katherine F. Jarvis ◽  
Joshua B. Kelley
Keyword(s):  
Viral Load ◽  
Temporal Dynamics ◽  
Reproduction Number ◽  
False Negative ◽  
False Negative Rate ◽  
Agent Based Model ◽  
Agent Based ◽  
Viral Spread ◽  
Negative Rate

AbstractColleges and other organizations are considering testing plans to return to operation as the COVID-19 pandemic continues. Pre-symptomatic spread and high false negative rates for testing may make it difficult to stop viral spread. Here, we develop a stochastic agent-based model of COVID-19 in a university sized population, considering the dynamics of both viral load and false negative rate of tests on the ability of testing to combat viral spread. Reported dynamics of SARS-CoV-2 can lead to an apparent false negative rate from ~ 17 to ~ 48%. Nonuniform distributions of viral load and false negative rate lead to higher requirements for frequency and fraction of population tested in order to bring the apparent Reproduction number (Rt) below 1. Thus, it is important to consider non-uniform dynamics of viral spread and false negative rate in order to model effective testing plans.

scholarly journals Preliminary Results of MyCog, a Brief Assessment for the Detection of Cognitive Impairment in Primary Care

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 259-260
Author(s):  
Laura Curtis ◽  
Lauren Opsasnick ◽  
Julia Yoshino Benavente ◽  
Cindy Nowinski ◽  
Rachel O’Conor ◽  
...  
Keyword(s):  
Primary Care ◽  
Cognitive Impairment ◽  
Cognitive Aging ◽  
False Positive ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Self Report ◽  
Card Sorting ◽  
Primary Care Settings

Abstract Early detection of Cognitive impairment (CI) is imperative to identify potentially treatable underlying conditions or provide supportive services when due to progressive conditions such as Alzheimer’s Disease. While primary care settings are ideal for identifying CI, it frequently goes undetected. We developed ‘MyCog’, a brief technology-enabled, 2-step assessment to detect CI and dementia in primary care settings. We piloted MyCog in 80 participants 65 and older recruited from an ongoing cognitive aging study. Cases were identified either by a documented diagnosis of dementia or mild cognitive impairment (MCI) or based on a comprehensive cognitive battery. Administered via an iPad, Step 1 consists of a single self-report item indicating concern about memory or other thinking problems and Step 2 includes two cognitive assessments from the NIH Toolbox: Picture Sequence Memory (PSM) and Dimensional Change Card Sorting (DCCS). 39%(31/80) participants were considered cognitively impaired. Those who expressed concern in Step 1 (n=52, 66%) resulted in a 37% false positive and 3% false negative rate. With the addition of the PSM and DCCS assessments in Step 2, the paradigm demonstrated 91% sensitivity, 75% specificity and an area under the ROC curve (AUC)=0.82. Steps 1 and 2 had an average administration time of <7 minutes. We continue to optimize MyCog by 1) examining additional items for Step 1 to reduce the false positive rate and 2) creating a self-administered version to optimize use in clinical settings. With further validation, MyCog offers a practical, scalable paradigm for the routine detection of cognitive impairment and dementia.

scholarly journals Ultrasound guided needle biopsy of axilla to evaluate nodal metastasis after preoperative systemic therapy in cohort of 106 breast cancers enriched with BRCA1/2 pathogenic variant carriers

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Baiba Līcīte ◽  
Arvīds Irmejs ◽  
Jeļena Maksimenko ◽  
Pēteris Loža ◽  
Genādijs Trofimovičs ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systemic Therapy ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Axillary Node ◽  
Pathogenic Variant ◽  
Axillary Lymph ◽  
Ultrasound Guided ◽  
Node Positive

Abstract Background Aim of the study is to evaluate the role of ultrasound guided fine needle aspiration cytology (FNAC) in the restaging of node positive breast cancer after preoperative systemic therapy (PST). Methods From January 2016 – October 2020 106 node positive stage IIA-IIIC breast cancer cases undergoing PST were included in the study. 18 (17 %) were carriers of pathogenic variant in BRCA1/2. After PST restaging of axilla was performed with ultrasound and FNAC of the marked and/or the most suspicious axillary node. In 72/106 cases axilla conserving surgery and in 34/106 cases axillary lymph node dissection (ALND) was performed. Results False Positive Rate (FPR) of FNAC after PST in whole cohort and BRCA1/2 positive subgroup is 8 and 0 % and False Negative Rate (FNR) – 43 and 18 % respectively. Overall Sensitivity − 55 %, specificity- 93 %, accuracy 70 %. Conclusion FNAC after PST has low FPR and is useful to predict residual axillary disease and to streamline surgical decision making regarding ALND both in BRCA1/2 positive and negative subgroups. FNR is high in overall cohort and FNAC alone are not able to predict ypCR and omission of further axillary surgery. However, FNAC performance in BRCA1/2 positive subgroup is more promising and further research with larger number of cases is necessary to confirm the results.

scholarly journals A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

2021 ◽  
Vol 13 (6) ◽  
pp. 1211
Author(s):  
Pan Fan ◽  
Guodong Lang ◽  
Bin Yan ◽  
Xiaoyan Lei ◽  
Pengju Guo ◽  
...  
Keyword(s):  
Vision System ◽  
False Positive Rate ◽  
Clustering Algorithms ◽  
False Negative ◽  
Color Space ◽  
False Negative Rate ◽  
Feature Selection Method ◽  
Image Features ◽  
Accurate Identification ◽  
Rgb Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

scholarly journals A new criterion for the alarm point using a combination of waveform amplitude and onset latency in Br(E)-MsEP monitoring in spine surgery

2018 ◽  
Vol 29 (4) ◽  
pp. 435-441 ◽  
Author(s):  
Kazuyoshi Kobayashi ◽  
Kei Ando ◽  
Ryuichi Shinjo ◽  
Kenyu Ito ◽  
Mikito Tsushima ◽  
...  
Keyword(s):  
Spine Surgery ◽  
Predictive Value ◽  
False Positive ◽  
Roc Analysis ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
High Rate ◽  
Motor Deficit ◽  
Onset Latency

OBJECTIVEMonitoring of brain evoked muscle-action potentials (Br[E]-MsEPs) is a sensitive method that provides accurate periodic assessment of neurological status. However, occasionally this method gives a relatively high rate of false-positives, and thus hinders surgery. The alarm point is often defined based on a particular decrease in amplitude of a Br(E)-MsEP waveform, but waveform latency has not been widely examined. The purpose of this study was to evaluate onset latency in Br(E)-MsEP monitoring in spinal surgery and to examine the efficacy of an alarm point using a combination of amplitude and latency.METHODSA single-center, retrospective study was performed in 83 patients who underwent spine surgery using intraoperative Br(E)-MsEP monitoring. A total of 1726 muscles in extremities were chosen for monitoring, and acceptable baseline Br(E)-MsEP responses were obtained from 1640 (95%). Onset latency was defined as the period from stimulation until the waveform was detected. Relationships of postoperative motor deficit with onset latency alone and in combination with a decrease in amplitude of ≥ 70% from baseline were examined.RESULTSNine of the 83 patients had postoperative motor deficits. The delay of onset latency compared to the control waveform differed significantly between patients with and without these deficits (1.09% ± 0.06% vs 1.31% ± 0.14%, p < 0.01). In ROC analysis, an intraoperative 15% delay in latency from baseline had a sensitivity of 78% and a specificity of 96% for prediction of postoperative motor deficit. In further ROC analysis, a combination of a decrease in amplitude of ≥ 70% and delay of onset latency of ≥ 10% from baseline had sensitivity of 100%, specificity of 93%, a false positive rate of 7%, a false negative rate of 0%, a positive predictive value of 64%, and a negative predictive value of 100% for this prediction.CONCLUSIONSIn spinal cord monitoring with intraoperative Br(E)-MsEP, an alarm point using a decrease in amplitude of ≥ 70% and delay in onset latency of ≥ 10% from baseline has high specificity that reduces false positive results.

Letters to the Editor

PEDIATRICS ◽  
1981 ◽  
Vol 68 (1) ◽  
pp. 144-145
Author(s):  
Lachlan Ch De Crespigny ◽  
Hugh P. Robinson
Keyword(s):  
Real Time ◽  
Linear Array ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Letters To The Editor ◽  
Premature Babies ◽  
Computed Tomography Scans ◽  
Positive Rate ◽  
Time Linear

We read with interest the report which suggested that the diagnosis of cerebroventricular hemorrhage ([CVH] including both subependymal [SEH] and intraventricular) with real time ultrasound was unreliable.1 Ultrasound, when compared with computed tomography scans, had a 35% false-positive rate and a 21% false-negative rate. In our institution over a 12-month period more than 200 premature babies have been examined (ADR real time linear array scanner with a 7-MHz transducer).

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