AI to Machine Learning: Lifeless Automation and Issues

Darshana, Subhashree; Rautaray, Siddharth Swarup; Pandey, Manjusha

doi:10.1007/978-981-33-6518-6_7

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Evolutionary algorithms have got wide range of applications such as in civil engineering [20], Machine learning [21,25]. Not only Evolutionary algorithms but other new models could be used to solve such dynamic problems such as AI [22], Deep Neural Network [23].This could be modelled as a multi-objective problem too and could be solved using multi-objective optimization [24] Factors influencing task allocation in DSD In Distributed Agile Software Development (DASD), task allocation is influenced by a wide array of factors spanning project and people-related considerations, expertise requirements, site characteristics, task attributes, and cost con-siderations. Effective coordination among distributed teams, prioritization based on business value, and addressing geographical and cultural differences are pivotal.…”

Section: Related Work Traditional Machine Learning Techniquesmentioning

confidence: 99%

GA Based Task Allocation In Agile Software Development In Distributed Environment

Nayak,

Jena,

Rout

2024

eatp

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Distributed Agile Software Development (DASD) merges Agile methodologies with globally dispersed teams, pre-senting challenges in effective task allocation due to geographical dispersion, cultural diversity, and communication barriers."This research introduces a quantitative approach to address these challenges, aiming to optimize task alloca-tion, resource utilization, and project outcomes in DASD." Task allocation within DASD teams is critical for managing project timelines, costs, and software quality. The proposed quantitative method considers various factors, including task requirements, team members' expertise levels, and historical performance data, to assign tasks effectively. By matching tasks with the most qualified experts, the approach seeks to enhance project efficiency and reduce rework. Coordination among distributed Agile teams is essential to prevent bottlenecks and foster team cohesion. Effec-tive communication tools and techniques are vital for bridging time zone differences and cultural diversity within DASD teams. Additionally, maintaining Agile principles while adapting to the distributed nature of teams is crucial for successful DASD implementation. Agile methodologies such as Scrum and Kanban promote iterative develop-ment, continuous feedback, and customer-centric focus. While Agile values remain fundamental, DASD requires adjustments to traditional Agile practices to accommodate distributed teams. By embracing remote collaboration techniques, organizations can deliver responsive software solutions while adhering to Agile principles. Overall, this research contributes to advancing task allocation practices in DASD, ultimately leading to improved customer sat-isfaction and project success. By providing a structured framework for task allocation, organizations can navigate the complexities of DASD more effectively, ensuring efficient resource utilization and timely project delivery. The proposed quantitative approach offers a valuable tool for optimizing task assignments, managing dependencies, and enhancing communication among distributed Agile teams.

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Section: Related Work Traditional Machine Learning Techniquesmentioning

confidence: 99%

GA Based Task Allocation In Agile Software Development In Distributed Environment

Nayak,

Jena,

Rout

2024

eatp

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Automated Cough Analysis with Convolutional Recurrent Neural Network

Wang,

Wahab,

Hong

et al. 2024

Bioengineering

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Chronic cough is associated with several respiratory diseases and is a significant burden on physical, social, and psychological health. Non-invasive, real-time, continuous, and quantitative monitoring tools are highly desired to assess cough severity, the effectiveness of treatment, and monitor disease progression in clinical practice and research. There are currently limited tools to quantitatively measure spontaneous coughs in daily living settings in clinical trials and in clinical practice. In this study, we developed a machine learning model for the detection and classification of cough sounds. Mel spectrograms are utilized as a key feature representation to capture the temporal and spectral characteristics of coughs. We applied this approach to automate cough analysis using 300 h of audio recordings from cough challenge clinical studies conducted in a clinical lab setting. A number of machine learning algorithms were studied and compared, including decision tree, support vector machine, k-nearest neighbors, logistic regression, random forest, and neural network. We identified that for this dataset, the CRNN approach is the most effective method, reaching 98% accuracy in identifying individual coughs from the audio data. These findings provide insights into the strengths and limitations of various algorithms, highlighting the potential of CRNNs in analyzing complex cough patterns. This research demonstrates the potential of neural network models in fully automated cough monitoring. The approach requires validation in detecting spontaneous coughs in patients with refractory chronic cough in a real-life setting.

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AI to Machine Learning: Lifeless Automation and Issues

Cited by 2 publications

References 29 publications

GA Based Task Allocation In Agile Software Development In Distributed Environment

GA Based Task Allocation In Agile Software Development In Distributed Environment

Automated Cough Analysis with Convolutional Recurrent Neural Network

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