Classification and Prediction of Software Incidents Using Machine Learning Techniques

Ali, Sikandar; Adeel, Muhammad; Johar, Sumaira; Zeeshan, Muhammad; Baseer, Samad; Irshad, Azeem

doi:10.1155/2021/9609823

Cited by 11 publications

(10 citation statements)

References 17 publications

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“…Traffic accidents have severe repercussions for traffic, especially on roads, at crossing, and in repainting regions, leading to increased traffic and emissions if accidents are not identified effectively and soon (Traffic Incident Management, 2013) [7]. (iv) Some of the motivating factors for vehicle detection include high computational complexity, loss of data sets, inordinate missteps start charging, detailed strength and investments of sensors on the side of lowering the mortality fee, and comprehensive power and assets of sensors on the side of lowering the mortality fee [20] hj is related to each weight member (Wi, j) of the matrix. In addition, bias weights (offsets) ai for vi and bj for hj are available.…”

Section: Problem Formulationmentioning

confidence: 99%

“…A modified RBM is a 2 Markov random field system that also includes a normal Markov field model. In the network, there are open and hidden gadgets that correspond to visible and hidden parameters, respectively [20]. Binary MRBM is a variant of MRBM wherein the variables in the visible and buried layers are binary.…”

Section: Training-modified Rbmmentioning

confidence: 99%

“…A modified RBM is a random field models with a 2 network system. e network is built up of visible (samples) and invisible (variables) components, respectively [20]. e MRBM is among the most widely used basic models in deep learning.…”

Section: Rbmmentioning

confidence: 99%

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Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Roohullah

Wahid

Ali

et al. 2022

Security and Communication Networks

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Accident detection in autonomous vehicles could save lives by reducing the time it takes for information to reach emergency responder. One of the most common reason for the death of humans is accident. Indeed, it was determined throughout the survey that road accidents are indeed the second greatest cause of death in the United States for people aged 30 to 44 years, representing for 1/3 among all deaths. The transportation industry is increasingly relying on mathematical methods and new data assets to detect injuries. Many machine learning and deep learning models have already been proposed for accident detection but still there is much space for further improvement to be done to save human lives in case of accident detection, if accidents are not identified well. In our present study, we proposed modified restricted Boltzmann machine for accident detection. Our proposed methodology consists of the following steps. In the first step, we took different accidental and nonaccidental images as an input. In the second step, we applied our proposed deep learning technique modified restricted Boltzmann machine. In the third step, when weight acceleration and coefficient adjustments are run as a generalization mechanism, then we check our model performance after applying through multiple procedures. As a result, multiple images are classified as accidental and nonaccidental images of vehicles. Proposed methodology has been applied for data set, and data have been divided into different training and testing ratios. The proposed MRBM model has an accuracy of 98% in classification of both accidental and nonaccidental images of vehicles. The proposed model outperforms the competition significantly than other in which they are compared like artificial neural network, support vector machine, and restricted Boltzmann machine techniques.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Training-modified Rbmmentioning

confidence: 99%

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Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Roohullah

Wahid

Ali

et al. 2022

Security and Communication Networks

Self Cite

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“…(1) Device RAM Memory (Rm), Result of Offloadies (Rs), ( 2) Energy (E), FCaompatibleDevice () is a function in Android to check for RAM, (3) Compatible Device (Cd), CPU, and Device minimum requirements. (4) Input: (Cr) (5) for each MEC Server do (6) Output: {Rm, E, CPU} (7) end for (7) for each Device d do (8) Calculate the Compatibility check using FCompatibleDevice (), add the device to the Cd list (9) end for (10) for each Cd do (11) Rs � Output {task} (12) end for ND.add (D {i}) (7) end if (8) if (i < DL.size() then (9) Move to Step 3 (10) Output {ND} (11) end if (12) MES Input {NCR} (13) end for (14) for each ND do (15) if ND {i}.ram>3 GB && ND {i}.CPU>2.4 GHz then (16) CD.add (ND {i}) (17) end if (18) if i< CD.size() then (19) Move to Step 9 (20) end if (21) Output {CD} (22) Input {T} (23) Calculate Result using function Compute_Result () (24) Output {Result} (25) end for ALGORITHM 3: Algorithm for optimization.…”

Section: Data Availabilitymentioning

confidence: 99%

Secured Optimized Resource Allocation in Mobile Edge Computing

Bibi¹,

Majeed²,

Ali

et al. 2022

Mobile Information Systems

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Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.

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“…While reiterating the applicability of data science in virtually all felds of human endeavor, Olaleye et al assertions in ref. [15] underscore the global research community's interest in adopting predictive analytics for resolving industry problems just like for software defect predictive modeling [16,17], as evident in literature across digital libraries consulted for the purpose of this work. Terefore, data science ofers a reliable functionality for scientifc studies which avail review studies a retinue of sources of investigations [18].…”

Section: Introductionmentioning

confidence: 95%

Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Olaleye

Arogundade

Misra

et al. 2023

Scientific Programming

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Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry.

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Classification and Prediction of Software Incidents Using Machine Learning Techniques

Cited by 11 publications

References 17 publications

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Secured Optimized Resource Allocation in Mobile Edge Computing

Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

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