Browsing by Author "Vaishnav, Jaimine"

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    Beyond the Headlines
    (Nex Gen Publications, 2024-04) Vaishnav, Jaimine
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    Contemporary Trends and Challenges and Advances, in the Manufacturing Industry, with a special focus on applications of Artificial Intelligence and Deep Learning
    (European Economic Letters, 2024) Grover, Pooja; Kumari, Sweta; Vaishnav, Jaimine; Mohammed, Shoaib
    In the last few years’ artificial intelligence (AI), has begun to make its appearance in our everyday life. Even though it is still in its early stage of development, AI has proved beyond human intelligence. DeepMind’s AlphaGo is an illustration of how the AI could provide amazing benefits, particularly in industries such as manufacturing. At the moment there are attempts to connect AI technology with precision engineering and manufacturing in order to change classical production methods. This research paper focuses on some notable milestones that have already been attained in the realization of AI for manufacturing and how it will change the face of any manufacturing facility. There are several challenges in the AI manufacturing application; these include data acquisition and management, human resources, infrastructure, security risks associated with trust issues as well implantation of hurdles. For instance, the collection of data required to train AI models can be challenging for rare events or expensive in large datasets that require labeling. The introduction of AI models into industrial control systems can also pose risks to the security, and some players in industry may be reluctant to use AI because they don’t trust it or understand what is going on. However, these hindrances do not deter AI from becoming an effective solution for predictive maintenance and quality assurance in the sector of manufacturing. Therefore, one should ponder over each manufacturing case and its needs before deciding if or how to adopt AI. The aim of this research paper is to analyze the current progress, problems and prospects in AI-ML across manufacturing entities. Its aim is to enhance knowledge of accessible technologies, support decision-making in choosing appropriate AI/ML technologies and determine where further research needs are possible centered on latest developments. Initial findings indicate that the combination of AI/ML technologies with advanced data collection capabilities from manufacturing networks can produce massive cost and efficiency gains. Though the accurate representation of complex phenomenon in manufacturing is problematic, AI can revolutionize this industry. Other areas where AI is intensively studied include medical image analysis, bioinformatics, recommendation systems and finance. Many notable AI products such as Amazon’s Alexa, IBM Watson and DeepMind AlphaGo have already integrated into our daily use. To address limitations such as interpretability and degraded performance with insufficient data, several sub-branches of deep learning are currently researched namely; Physics - Informed Deep Learning (PIDL), Explainable AI(XAI), Domain Adaptation, (DA) Active Leaning (AL), Multi Task Learning MTL, Graph Neural Network GNN. Convergence of AI with other engineering industries have a potential issue that should not be ignored. The aim is to enable an effective use of AI by the precision engineering and manufacturing community for future-oriented manufacture.
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    Informal Trade: China at India's North-East Border
    (Empyreal Publishing House, 2023-04) Vaishnav, Jaimine
    This may sound a little polemic but India’s approach to her own territories in the Northeast region has met with military, conditional packages, and limited communication. The quantum of growth and development, on par with other states in the cow-belt region or southern states, has always been a matter of debate, ignorance, and issues. This is not to deny the secessionist politics in the parts of the North-East region, which backfired at the serenity of Article 1 of the Indian Constitution. But to deny representation and economic activism, without the advocacy of bullets, to the people of the North-east region is anti-national. This book is a precise attempt, with proven facts and data, interviews, and suggestions to boost the trade prospects at the border of India-China. Historically, this trade route has been an ideal osmosis between India and China, and other regions of the world. With the backlash in the year 1962, the trade came to a halt till the year 2006. Since then the government of India and China, including the geopolitical concerns, has been gradual to boost the trade prospects. The cases of informal trade are also reported here, due to statism. Dr. Mahendra Lama, in the past, was appointed to study this trade circle. However, his data were a little exaggerated. The dire need, in this trade activities, needs a libertarian touch so as to also diffuse the concerns of trade and disputes between India and China
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    Profit Pathways
    (Shine Book Publishing, 2024) Vaishnav, Jaimine; Mohammed, Shoaib
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    Understanding multi-cloud platform: innovative AI-assisted trust-aware resource allocation technique
    (Springer Nature, 2025-01-11) Vaishnav, Jaimine
    Resource allocation in a cloud context is a major challenge in terms of cost and quality optimization. To enhance customer assistance, cloud service providers (CSPs) need to consider quality-related factors. This study addresses this gap by integrating both trust and delay into a resource allocation approach supported by artificial intelligence (AI). By considering factors such as availability, effectiveness, stability, and data accuracy, CSP trustworthiness can be evaluated. The aim is to minimize communication delay while maximizing trust in resource allocation through a dynamic trust-aware intelligent water drop (DTIWD) optimization method. The DTIWD method is suggested utilizing a range of features that provide adaptability to different services. The method dynamically allocates resources based on the CSP’s trust attributes, promoting flexibility across different service requirements. Experimental results show that the DTIWD method achieves optimal performance, proving effective in maintaining resource allocation across metrics such as availability, data integrity, and time efficiency. When tested under different workloads, the approach demonstrates high adaptability, ensuring reliable service even with varying loads. The method also shows significant improvement in time efficiency, maintaining data integrity while effectively managing resources for different availability demands. In addition, by allocating resources effectively, incorporating trust into the resource allocation framework contributes to improved balanced weighting between trust (0.5) and delay (0.5) yields improved scalability and interoperability a reduction in communication delays during the selection procedure. Future work could explore the integration of blockchain technology to further enhance trust management and scalability in multi-cloud resource allocation systems.