Contrasting Parallelized with Sequential Sorting

Dutta, Anurag; Saha, Manideepa

doi:10.1109/icraie56454.2022.10054300

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…The bank expanded its presence in technological hotspots (Yang and Yu 2020) around the globe. Ken Wilcox became CEO in 2000 and decided to keep the company's exclusive emphasis on technology providers (Dutta et al 2023b;Dutta and Saha 2022) instead of broadening into a commercial and industrial financial institution. Silicon Valley Bank explicitly joined the personal financial services sector in 2002, capitalizing on existing knowledge and ties with affluent pioneering investors and entrepreneurs.…”

Section: Remembrance-a Dive Into the Pastmentioning

confidence: 99%

The Silicon Valley Bank Failure: Application of Benford’s Law to Spot Abnormalities and Risks

Dutta,

Voumik,

Kumarasankaralingam

et al. 2023

Risks

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Data are produced every single instant in the modern era of technological breakthroughs we live in today and is correctly termed as the lifeblood of today’s world; whether it is Google or Meta, everyone depends on data to survive. But, with the immense surge in technological boom comes several backlashes that tend to pull it down; one similar instance is the data morphing or modification of the data unethically. In many jurisdictions, the phenomenon of data morphing is considered a severe offense, subject to lifelong imprisonment. There are several cases where data are altered to encrypt reliable details. Recently, in March 2023, Silicon Valley Bank collapsed following unrest prompted by increasing rates. Silicon Valley Bank ran out of money as entrepreneurial investors pulled investments to maintain their businesses afloat in a frigid backdrop for IPOs and individual financing. The bank’s collapse was the biggest since the financial meltdown of 2008 and the second-largest commercial catastrophe in American history. By confirming the “Silicon Valley Bank” stock price data, we will delve further into the actual condition of whether there has been any data morphing in the data put forward by the Silicon Valley Bank. To accomplish the very same, we applied a very well-known statistical paradigm, Benford’s Law and have cross-validated the results using comparable statistics, like Zipf’s Law, to corroborate the findings. Benford’s Law has several temporal proximities, known as conformal ranges, which provide a closer examination of the extent of data morphing that has occurred in the data presented by the various organizations. In this research for validating the stock price data, we have considered the opening, closing, and highest prices of stocks for a time frame of 36 years, between 1987 and 2023. Though it is worth mentioning that the data used for this research are coarse-grained, still since the validation is subjected to a larger time horizon of 36 years; Benford’s Law and the similar statistics used in this article can point out any irregularities, which can result in some insight into the situation and into whether there has been any data morphing in the Stock Price data presented by SVB or not. This research has clearly shown that the stock price variations of the SVB diverge much from the permissible ranges, which can give a conclusive direction on further investigations in this issue by the responsible authorities. In addition, readers of this article must note that the conclusion formed about the topic discussed in this article is objective and entirely based on statistical analysis and factual figures presented by the Silicon Valley Bank Group.

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Section: Remembrance-a Dive Into the Pastmentioning

confidence: 99%

The Silicon Valley Bank Failure: Application of Benford’s Law to Spot Abnormalities and Risks

Dutta,

Voumik,

Kumarasankaralingam

et al. 2023

Risks

Self Cite

View full text Add to dashboard Cite

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“…The objective is to maintain specified tumor coverage while primarily satisfying clinical dose restrictions linked to healthy organs. The desired function for an RT scheduling optimization challenge is often created using specified tumor reimbursement and organ-based dose restrictions [12]. Although organ-based dosage restrictions depend on the person receiving treatment, treatment protocols are created and amended by multi-institutional collaborations like the radiation therapy oncology group (RTOG) report recommendations.…”

Section: Introductionmentioning

confidence: 99%

Establishing Radiotherapy on a Hybridization of Genetic Algorithms, and Particle Swarm Optimization

2023

Preprint

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<p> In large-scale, non-convex problems, adaptive stochastic global optimization methodologies are frequently used. But improving these methods’ search effectiveness and re- peatability frequently calls for well-customized methodologies. Intensity-modulated radiation treatment (IMRT) planning faces the critical but difficult problem of automatically selecting the beam angle. Despite numerous efforts, the clinical IMRT practice continues to be not particularly satisfying for the reason of the excessive processing of the inverse circumstance. The objective problem that we would discuss in this research is the 4-Dimensional Radiation Therapy (4DRT) Inverse Planning Problem. Previous research in this domain makes use of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This article aims in proposing a hybrid model that combines both the Genetic Algorithm as well as the Particle Swarm Optimization and chooses the best resulting parameters. The proposition makes use of the concept of threading that allows both the GA, and the PSO to run in parallel at the same time. </p>

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