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1,023 results on '"Ghuge, A"'

2. Stochastic Scheduling with Abandonments via Greedy Strategies

Author

Xu, Yihua, Ghuge, Rohan, and Perez-Salazar, Sebastian

Subjects
Mathematics - Optimization and Control, Computer Science - Data Structures and Algorithms, Computer Science - Computer Science and Game Theory
Abstract

Motivated by applications where impatience is pervasive and service times are uncertain, we study a scheduling model where jobs may depart at an unknown point in time and service times are stochastic. Initially, we have access to a single server and $n$ jobs with known non-negative values: these jobs have unknown stochastic service and departure times with known distributional information, which we assume to be independent. When the server is free, we can run an available job which occupies the server for an unknown amount of time, and collect its value. The objective is to maximize the expected total value obtained from jobs run on the server. Natural formulations of this problem suffer from the curse of dimensionality. In fact, this problem is NP-hard even in the deterministic case. Hence, we focus on efficiently computable approximation algorithms that can provide high expected reward compared to the optimal expected value. Towards this end, we first provide a compact linear programming (LP) relaxation that gives an upper bound on the expected value obtained by the optimal policy. Then we design a polynomial-time algorithm that is nearly a $(1/2)\cdot (1-1/e)$-approximation to the optimal LP value (so also to the optimal expected value). We next shift our focus to the case of independent and identically distributed (i.i.d.) service times. In this case, we show that the greedy policy that always runs the highest-valued job whenever the server is free obtains a $1/2$-approximation to the optimal expected value. Our approaches extend effortlessly and we demonstrate their flexibility by providing approximations to natural extensions of our problem. Finally, we evaluate our LP-based policies and the greedy policy empirically on synthetic and real datasets.

Published
2024

3. Developing Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models?

Author

Raza, Shaina, Bamgbose, Oluwanifemi, Ghuge, Shardul, Tavakol, Fatemeh, Reji, Deepak John, and Bashir, Syed Raza

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) have advanced various Natural Language Processing (NLP) tasks, such as text generation and translation, among others. However, these models often generate text that can perpetuate biases. Existing approaches to mitigate these biases usually compromise knowledge retention. This study explores whether LLMs can produce safe, unbiased outputs without sacrificing knowledge or comprehension. We introduce the Safe and Responsible Large Language Model (\textbf{SR}$_{\text{LLM}}$), which has been instruction fine-tuned atop an inherently safe fine-tuned LLM to reduce biases in generated texts. We developed a specialized dataset with examples of unsafe and corresponding safe variations to train \textbf{SR}$_{\text{LLM}}$ to identify and correct biased text. Experiments on our specialized dataset and out-of-distribution test sets reveal that \textbf{SR}$_{\text{LLM}}$ effectively reduces biases while preserving knowledge integrity. This performance surpasses that of traditional fine-tuning of smaller language models and base LLMs that merely reply on prompting techniques. Our findings indicate that instruction fine-tuning is an effective strategy for minimizing bias in LLMs while retaining knowledge. The code and dataset are accessible at \href{https://github.com/shainarazavi/Safe-Responsible-LLM}{SR-LLM}.

Published
2024

5. FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?

Author

Raza, Shaina, Ghuge, Shardul, Ding, Chen, Dolatabadi, Elham, and Pandya, Deval

Subjects
Computer Science - Computation and Language
Abstract

The rapid evolution of Large Language Models (LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data principles. While these principles are crucial for ethical data stewardship, their specific application in the context of LLM training data remains an under-explored area. This research gap is the focus of our study, which begins with an examination of existing literature to underline the importance of FAIR principles in managing data for LLM training. Building upon this, we propose a novel framework designed to integrate FAIR principles into the LLM development lifecycle. A contribution of our work is the development of a comprehensive checklist intended to guide researchers and developers in applying FAIR data principles consistently across the model development process. The utility and effectiveness of our framework are validated through a case study on creating a FAIR-compliant dataset aimed at detecting and mitigating biases in LLMs. We present this framework to the community as a tool to foster the creation of technologically advanced, ethically grounded, and socially responsible AI models., Comment: Accepted

Published
2024

6. Semi-Bandit Learning for Monotone Stochastic Optimization

Author

Agarwal, Arpit, Ghuge, Rohan, and Nagarajan, Viswanath

Subjects
Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms
Abstract

Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area. However, a significant limitation of this approach is that it requires full knowledge of the underlying probability distributions. Can we still get good (approximation) algorithms if these distributions are unknown, and the algorithm needs to learn them through repeated interactions? In this paper, we resolve this question for a large class of "monotone" stochastic problems, by providing a generic online learning algorithm with $\sqrt{T \log T}$ regret relative to the best approximation algorithm (under known distributions). Importantly, our online algorithm works in a semi-bandit setting, where in each period, the algorithm only observes samples from the r.v.s that were actually probed. Our framework applies to several fundamental problems in stochastic optimization such as prophet inequality, Pandora's box, stochastic knapsack, stochastic matchings and stochastic submodular optimization.

Published
2023

7. Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time

Author

Raza, Shaina, Rahman, Mizanur, and Ghuge, Shardul

Subjects
Computer Science - Computation and Language
Abstract

Despite increasing awareness and research around fake news, there is still a significant need for datasets that specifically target racial slurs and biases within North American political speeches. This is particulary important in the context of upcoming North American elections. This study introduces a comprehensive dataset that illuminates these critical aspects of misinformation. To develop this fake news dataset, we scraped and built a corpus of 40,000 news articles about political discourses in North America. A portion of this dataset (4000) was then carefully annotated, using a blend of advanced language models and human verification methods. We have made both these datasets openly available to the research community and have conducted benchmarking on the annotated data to demonstrate its utility. We release the best-performing language model along with data. We encourage researchers and developers to make use of this dataset and contribute to this ongoing initiative.

Published
2023

10. Informative Path Planning with Limited Adaptivity

Author

Tan, Rayen, Ghuge, Rohan, and Nagarajan, Viswanath

Subjects
Computer Science - Data Structures and Algorithms
Abstract

We consider the informative path planning ($\mathtt{IPP}$) problem in which a robot interacts with an uncertain environment and gathers information by visiting locations. The goal is to minimize its expected travel cost to cover a given submodular function. Adaptive solutions, where the robot incorporates all available information to select the next location to visit, achieve the best objective. However, such a solution is resource-intensive as it entails recomputing after every visited location. A more practical approach is to design solutions with a small number of adaptive "rounds", where the robot recomputes only once at the start of each round. In this paper, we design an algorithm for $\mathtt{IPP}$ parameterized by the number $k$ of adaptive rounds, and prove a smooth trade-off between $k$ and the solution quality (relative to fully adaptive solutions). We validate our theoretical results by experiments on a real road network, where we observe that a few rounds of adaptivity suffice to obtain solutions of cost almost as good as fully-adaptive ones., Comment: 35 pages, 9 figures

Published
2023

11. Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis

Author

Raza, Shaina, Bamgbose, Oluwanifemi, Chatrath, Veronica, Ghuge, Shardul, Sidyakin, Yan, and Muaad, Abdullah Y

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the Contextualized Bi-Directional Dual Transformer (CBDT) \textcolor{green}{\faLeaf} classifier. This model combines two complementary transformer networks: the Context Transformer and the Entity Transformer, with a focus on improving bias detection capabilities. We have prepared a dataset specifically for training these models to identify and locate biases in texts. Our evaluations across various datasets demonstrate CBDT \textcolor{green} effectiveness in distinguishing biased narratives from neutral ones and identifying specific biased terms. This work paves the way for applying the CBDT \textcolor{green} model in various linguistic and cultural contexts, enhancing its utility in bias detection efforts. We also make the annotated dataset available for research purposes., Comment: Accepted in IEEE Transactions on Computational Social Systems

Published
2023

13. Single Smart Card for Identity and Authentication Using Data Analytics

Author

Sawant, Sachin, Ghodke, Siddhant, Ghuge, Dnyaneshwari, Ghodake, Kanishka, Ghodake, Shivam, Ghuge, Aditya, Ghuge, Darshan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Rajesh, editor, Verma, Ajit Kumar, editor, Verma, Om Prakash, editor, and Wadehra, Tanu, editor

Published
2024
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14. Sample Size Determination in Randomised Controlled Clinical Trials: A Narrative Review

Author

Rakesh Diliprao Kothavale, Yogesh Pradip Patil, Savita Shamkant Mahajan, and Yogita Arun Ghuge

Subjects
biostatistics, research design, researchers, Medicine
Abstract

This article summarises efforts towards understanding the calculation of sample size determination in Randomised controlled Clinical Trials (RCTs). Readers would gain insight into the procedures behind sample size calculation in RCTs and its significance. Additionally, researchers, including residents, will learn to calculate sample sizes independently, aiding in their postgraduate thesis or dissertation work, especially for straightforward projects. The determination of sample size depends on various factors, several of which are discussed in this article. The concepts of biostatistics, which underpin sample size calculations and other statistical computations, are also elucidated. Examples are provided to illustrate how formula variations correspond to changes in influencing factors. Furthermore, a list of relevant software tools is included.

Published
2024
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18. Smart Contract Vulnerability Auditor Using GNN

Author

Bharadwaj, Rakhi, Kadam, Vaibhav, Kachare, Mitali, Jagtap, Rahul, Ghuge, Pranjal, Uttarwar, Varad, Vaze, Sanaya, Urane, Samarth, Tambe, Riya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Jagdish Chand, editor, Borah, Samarjeet, editor, Hussain, Shahid, editor, and Salhi, Said, editor

Published
2024
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19. An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem

Author

Agarwal, Arpit, Ghuge, Rohan, and Nagarajan, Viswanath

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We study the $K$-armed dueling bandit problem, a variation of the traditional multi-armed bandit problem in which feedback is obtained in the form of pairwise comparisons. Previous learning algorithms have focused on the $\textit{fully adaptive}$ setting, where the algorithm can make updates after every comparison. The "batched" dueling bandit problem is motivated by large-scale applications like web search ranking and recommendation systems, where performing sequential updates may be infeasible. In this work, we ask: $\textit{is there a solution using only a few adaptive rounds that matches the asymptotic regret bounds of the best sequential algorithms for $K$-armed dueling bandits?}$ We answer this in the affirmative $\textit{under the Condorcet condition}$, a standard setting of the $K$-armed dueling bandit problem. We obtain asymptotic regret of $O(K^2\log^2(K)) + O(K\log(T))$ in $O(\log(T))$ rounds, where $T$ is the time horizon. Our regret bounds nearly match the best regret bounds known in the fully sequential setting under the Condorcet condition. Finally, in computational experiments over a variety of real-world datasets, we observe that our algorithm using $O(\log(T))$ rounds achieves almost the same performance as fully sequential algorithms (that use $T$ rounds).

Published
2022

22. Batched Dueling Bandits

Author

Agarwal, Arpit, Ghuge, Rohan, and Nagarajan, Viswanath

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied. Previous works have only focused on the sequential setting where the policy adapts after every comparison. However, in many applications such as search ranking and recommendation systems, it is preferable to perform comparisons in a limited number of parallel batches. We study the batched $K$-armed dueling bandit problem under two standard settings: (i) existence of a Condorcet winner, and (ii) strong stochastic transitivity and stochastic triangle inequality. For both settings, we obtain algorithms with a smooth trade-off between the number of batches and regret. Our regret bounds match the best known sequential regret bounds (up to poly-logarithmic factors), using only a logarithmic number of batches. We complement our regret analysis with a nearly-matching lower bound. Finally, we also validate our theoretical results via experiments on synthetic and real data.

Published
2022

25. Non-Adaptive Stochastic Score Classification and Explainable Halfspace Evaluation

Author

Ghuge, Rohan, Gupta, Anupam, and Nagarajan, Viswanath

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Sequential testing problems involve a complex system with several components, each of which is "working" with some independent probability. The outcome of each component can be determined by performing a test, which incurs some cost. The overall system status is given by a function $f$ of the outcomes of its components. The goal is to evaluate this function $f$ by performing tests at the minimum expected cost. While there has been extensive prior work on this topic, provable approximation bounds are mainly limited to simple functions like ``k-out-of-n'' and halfspaces. We consider significantly more general "score classification" functions, and provide the first constant factor approximation algorithm (improving over a previous logarithmic approximation ratio). Moreover, our policy is non adaptive: it just involves performing tests in an a priori fixed order. We also consider the related halfspace evaluation problem, where we want to evaluate some function on $d$ halfspaces (e.g., intersection of halfspaces). We show that our approach provides an $O(d^2\log d)$-approximation algorithm for this problem. Our algorithms also extend to the setting of "batched'' tests, where multiple tests can be performed simultaneously while incurring an extra setup cost. Finally, we perform computational experiments that demonstrate the practical performance of our algorithm for score classification. We observe that, for most instances, the cost of our algorithm is within $50\%$ of an information-theoretic lower bound on the optimal value., Comment: Full version of IPCO 2022 paper

Published
2021

28. The Power of Adaptivity for Stochastic Submodular Cover

Author

Ghuge, Rohan, Gupta, Anupam, and Nagarajan, Viswanath

Subjects
Computer Science - Data Structures and Algorithms
Abstract

In the stochastic submodular cover problem, the goal is to select a subset of stochastic items of minimum expected cost to cover a submodular function. Solutions in this setting correspond to sequential decision processes that select items one by one "adaptively" (depending on prior observations). While such adaptive solutions achieve the best objective, the inherently sequential nature makes them undesirable in many applications. We ask: how well can solutions with only a few adaptive rounds approximate fully-adaptive solutions? We give nearly tight answers for both independent and correlated settings, proving smooth tradeoffs between the number of adaptive rounds and the solution quality, relative to fully adaptive solutions. Experiments on synthetic and real datasets show qualitative improvements in the solutions as we allow more rounds of adaptivity; in practice, solutions with a few rounds of adaptivity are nearly as good as fully adaptive solutions., Comment: In proceedings of ICML 2021

Published
2021

29. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Author

Schumacher, Austin E, Kyu, Hmwe Hmwe, Aali, Amirali, Abbafati, Cristiana, Abbas, Jaffar, Abbasgholizadeh, Rouzbeh, Abbasi, Madineh Akram, Abbasian, Mohammadreza, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed, Abdollahi, Mohammad, Abdoun, Meriem, Abdullahi, Auwal, Abdurehman, Ame Mehadi, Abebe, Mesfin, Abedi, Aidin, Abedi, Armita, Abegaz, Tadesse M, Abeldaño Zuñiga, Roberto Ariel, Abhilash, E S, Abiodun, Olugbenga Olusola, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abrigo, Michael R M, Abtahi, Dariush, Abu Rumeileh, Samir, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Acuna, Juan Manuel, Adair, Tim, Addo, Isaac Yeboah, Adebayo, Oladimeji M, Adegboye, Oyelola A, Adekanmbi, Victor, Aden, Bashir, Adepoju, Abiola Victor, Adetunji, Charles Oluwaseun, Adeyeoluwa, Temitayo Esther, Adeyomoye, Olorunsola Israel, Adha, Rishan, Adibi, Amin, Adikusuma, Wirawan, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afework, Abel, Afolabi, Aanuoluwapo Adeyimika, Afraz, Ali, Afyouni, Shadi, Afzal, Saira, Agasthi, Pradyumna, Aghamiri, Shahin, Agodi, Antonella, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Tauseef, Ahmadi, Keivan, Ahmadzade, Amir Mahmoud, Ahmadzade, Mohadese, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Muktar Beshir, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Ajumobi, Olufemi, Akalu, Gizachew Taddesse, Akara, Essona Matatom, Akinosoglou, Karolina, Akkala, Sreelatha, Akyirem, Samuel, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Homsi, Ammar, Al Qadire, Mohammad, Ala, Moein, Aladelusi, Timothy Olukunle, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Al-Aly, Ziyad, Alam, Khurshid, Alam, Manjurul, Alam, Zufishan, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albashtawy, Mohammed, AlBataineh, Mohammad T, Aldridge, Robert W, Alemi, Sharifullah, Al-Eyadhy, Ayman, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhalaiqa, Fadwa Alhalaiqa Naji, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Akhtar, Ali, Beriwan Abdulqadir, Ali, Hassam, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Zahid, Alian Samakkhah, Shohreh, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aligol, Mohammad, Alimi, Rasoul, Aliyi, Ahmednur Adem, Al-Jumaily, Adel, Aljunid, Syed Mohamed, Almahmeed, Wael, Al-Marwani, Sabah, Al-Maweri, Sadeq Ali Ali, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Omar, Alomari, Mahmoud A, Alonso, Nivaldo, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Sabah, Salman Khalifah, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Alvi, Farrukh Jawad, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Alzoubi, Karem H, Amare, Azmeraw T, Ameyaw, Edward Kwabena, Amhare, Abebe Feyissa, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Aminian Dehkordi, Javad, Amiri, Sohrab, Amu, Hubert, Amugsi, Dickson A, Amzat, Jimoh, Ancuceanu, Robert, Anderlini, Deanna, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Angappan, Dhanalakshmi, Anil, Abhishek, Anjum, Afifa, Antony, Catherine M, Antriyandarti, Ernoiz, Anuoluwa, Iyadunni Adesola, Anwar, Sumadi Lukman, Anyasodor, Anayochukwu Edward, Appiah, Seth Christopher Yaw, Aqeel, Muhammad, Arabloo, Jalal, Arabzadeh Bahri, Razman, Arab-Zozani, Morteza, Arafat, Mosab, Araújo, Ana Margarida, Aravkin, Aleksandr Y, Aremu, Abdulfatai, Ariffin, Hany, Aripov, Timur, Armocida, Benedetta, Arooj, Mahwish, Artamonov, Anton A, Artanti, Kurnia Dwi, Arulappan, Judie, Aruleba, Idowu Thomas, Aruleba, Raphael Taiwo, Arumugam, Ashokan, Asaad, Malke, Asgary, Saeed, Ashemo, Mubarek Yesse, Ashraf, Muhammad, Asika, Marvellous O, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Atreya, Alok, Attia, Sameh, Aujayeb, Avinash, Avan, Abolfazl, Awotidebe, Adedapo Wasiu, Ayala Quintanilla, Beatriz Paulina, Ayanore, Martin Amogre, Ayele, Getnet Melaku, Ayuso-Mateos, Jose L, Ayyoubzadeh, Seyed Mohammad, Azadnajafabad, Sina, Azhar, Gulrez Shah, Aziz, Shahkaar, Azzam, Ahmed Y, Babashahi, Mina, Babu, Abraham Samuel, Badar, Muhammad, Badawi, Alaa, Badiye, Ashish D, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bah, Sulaiman, Bahadorikhalili, Saeed, Bai, Jianjun, Bai, Ruhai, Baker, Jennifer L, Bakkannavar, Shankar M, Bako, Abdulaziz T, Balakrishnan, Senthilkumar, Balogun, Saliu A, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Bandyopadhyay, Soham, Banik, Biswajit, Banik, Palash Chandra, Bansal, Hansi, Barati, Shirin, Barchitta, Martina, Bardhan, Mainak, Barker-Collo, Suzanne Lyn, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barr, Ronald D, Barrero, Lope H, Basharat, Zarrin, Bashir, Asma'u I J, Bashiru, Hameed Akande, Baskaran, Pritish, Basnyat, Buddha, Bassat, Quique, Basso, João Diogo, Basu, Saurav, Batra, Kavita, Batra, Ravi, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Beaney, Thomas, Bedi, Neeraj, Begum, Tahmina, Behboudi, Emad, Behnoush, Amir Hossein, Beiranvand, Maryam, Bejarano Ramirez, Diana Fernanda, Belgaumi, Uzma Iqbal, Bell, Michelle L, Bello, Aminu K, Bello, Muhammad Bashir, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bendak, Salaheddine, Bennett, Derrick A, Bensenor, Isabela M, Benzian, Habib, Berezvai, Zombor, Berman, Adam E, Bermudez, Amiel Nazer C, Bettencourt, Paulo J G, Beyene, Habtamu B, Beyene, Kebede A, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhalla, Ashish, Bhandari, Dinesh, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhardwaj, Prarthna V, Bhargava, Ashish, Bhaskar, Sonu, Bhat, Vivek, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhatti, Rajbir, Bhutta, Zulfiqar A, Bikbov, Boris, Binmadi, Nada, Bintoro, Bagas Suryo, Biondi, Antonio, Bisignano, Catherine, Bisulli, Francesca, Biswas, Atanu, Biswas, Raaj Kishore, Bitaraf, Saeid, Bjørge, Tone, Bleyer, Archie, Boampong, Mary Sefa, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bolarinwa, Obasanjo Afolabi, Bonakdar Hashemi, Milad, Bonny, Aime, Bora, Kaustubh, Bora Basara, Berrak, Borodo, Safiya Bala, Borschmann, Rohan, Botero Carvajal, Alejandro, Bouaoud, Souad, Boudalia, Sofiane, Boyko, Edward J, Bragazzi, Nicola Luigi, Braithwaite, Dejana, Brenner, Hermann, Britton, Gabrielle, Browne, Annie J, Brunoni, Andre R, Bulamu, Norma B, Bulto, Lemma N, Buonsenso, Danilo, Burkart, Katrin, Burns, Richard A, Burugina Nagaraja, Sharath, Busse, Reinhard, Bustanji, Yasser, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Cai, Tianji, Calina, Daniela, Cámera, Luis Alberto, Campos, Luciana Aparecida, Campos-Nonato, Ismael R, Cao, Chao, Cardenas, Carlos Alberto, Cárdenas, Rosario, Carr, Sinclair, Carreras, Giulia, Carrero, Juan J, Carugno, Andrea, Carvalho, Felix, Carvalho, Márcia, Castaldelli-Maia, Joao Mauricio, Castañeda-Orjuela, Carlos A, Castelpietra, Giulio, Catalá-López, Ferrán, Catapano, Alberico L, Cattaruzza, Maria Sofia, Caye, Arthur, Cederroth, Christopher R, Cembranel, Francieli, Cenderadewi, Muthia, Cercy, Kelly M, Cerin, Ester, Cevik, Muge, Chacón-Uscamaita, Pamela R Uscamaita, Chahine, Yaacoub, Chakraborty, Chiranjib, Chan, Jeffrey Shi Kai, Chang, Chin-Kuo, Charalampous, Periklis, Charan, Jaykaran, Chattu, Vijay Kumar, Chatzimavridou-Grigoriadou, Victoria, Chavula, Malizgani Paul, Cheema, Huzaifa Ahmad, Chen, An-Tian, Chen, Haowei, Chen, Lingxiao, Chen, Meng Xuan, Chen, Simiao, Cherbuin, Nicolas, Chew, Derek S, Chi, Gerald, Chirinos-Caceres, Jesus Lorenzo, Chitheer, Abdulaal, Cho, So Mi Jemma, Cho, William C S, Chong, Bryan, Chopra, Hitesh, Choudhary, Rahul, Chowdhury, Rajiv, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Chung, Eric, Chung, Eunice, Chung, Sheng-Chia, Cini, Karly I, Clark, Cain C T, Coberly, Kaleb, Columbus, Alyssa, Comfort, Haley, Conde, Joao, Conti, Sara, Cortesi, Paolo Angelo, Costa, Vera Marisa, Cousin, Ewerton, Cowden, Richard G, Criqui, Michael H, Cruz-Martins, Natália, Culbreth, Garland T, Cullen, Patricia, Cunningham, Matthew, da Silva e Silva, Daniel, Dadana, Sriharsha, Dadras, Omid, Dai, Zhaoli, Dalal, Koustuv, Dalli, Lachlan L, Damiani, Giovanni, D'Amico, Emanuele, Daneshvar, Sara, Darwesh, Aso Mohammad, Das, Jai K, Das, Saswati, Dash, Nihar Ranjan, Dashti, Mohsen, Dávila-Cervantes, Claudio Alberto, Davis Weaver, Nicole, Davletov, Kairat, De Leo, Diego, Debele, Aklilu Tamire, Degenhardt, Louisa, Dehbandi, Reza, Deitesfeld, Lee, Delgado-Enciso, Ivan, Delgado-Ortiz, Laura, Demant, Daniel, Demessa, Berecha Hundessa, Demetriades, Andreas K, Deng, Xinlei, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Des Jarlais, Don C, Desai, Hardik Dineshbhai, Desai, Rupak, Deuba, Keshab, Devanbu, Vinoth Gnana Chellaiyan, Dey, Sourav, Dhali, Arkadeep, Dhama, Kuldeep, Dhimal, Mandira Lamichhane, Dhimal, Meghnath, Dhingra, Sameer, Dias da Silva, Diana, Diaz, Daniel, Dima, Adriana, Ding, Delaney D, Dirac, M Ashworth, Dixit, Abhinav, Dixit, Shilpi Gupta, Do, Thanh Chi, Do, Thao Huynh Phuong, do Prado, 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2024
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30. Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

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Bhattacharjee, Natalia V, Schumacher, Austin E, Aali, Amirali, Abate, Yohannes Habtegiorgis, Abbasgholizadeh, Rouzbeh, Abbasian, Mohammadreza, Abbasi-Kangevari, Mohsen, Abbastabar, Hedayat, Abd ElHafeez, Samar, Abd-Elsalam, Sherief, Abdollahi, Mohammad, Abdollahifar, Mohammad-Amin, Abdoun, Meriem, Abdullahi, Auwal, Abebe, Mesfin, Abebe, Samrawit Shawel, Abiodun, Olumide, Abolhassani, Hassan, Abolmaali, Meysam, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abrigo, Michael R M, Abtahi, Dariush, Abualruz, Hasan, Abubakar, Bilyaminu, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Adal, Tadele Girum Girum, Adane, Mesafint Molla, Adeagbo, Oluwafemi Atanda Adeagbo, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Aden, Bashir, Adepoju, Abiola Victor, Adetokunboh, Olatunji O, Adetunji, Juliana Bunmi, Adeyinka, Daniel Adedayo, Adeyomoye, Olorunsola Israel, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afolabi, Rotimi Felix, Afyouni, Shadi, Afzal, Muhammad Sohail, Afzal, Saira, Aghamiri, Shahin, Agodi, Antonella, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahlstrom, Austin J, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Tauseef, Ahmed, Ali, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Meqdad Saleh, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Akalu, Gizachew Taddesse, Akbarialiabad, Hossein, Akinyemi, Rufus Olusola, Akkaif, Mohammed Ahmed, Akkala, Sreelatha, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Qadire, Mohammad, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albakri, Almaza, Albashtawy, Mohammed, AlBataineh, Mohammad T, Alemi, Hediyeh, Alemi, Sharifullah, Alemu, Yihun Mulugeta, Al-Eyadhy, Ayman, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhajri, Noora, Alhalaiqa, Fadwa Alhalaiqa Naji, Alhassan, Robert Kaba, Ali, Abid, Ali, Beriwan Abdulqadir, Ali, Liaqat, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Alif, Sheikh Mohammad, Aligol, Mohammad, Alijanzadeh, Mehran, Aljasir, Mohammad A M, Aljunid, Syed Mohamed, Al-Marwani, Sabah, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Omar, Alomari, Mahmoud A, Al-Omari, Basem, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Raddadi, Rajaa M, Al-Sabah, Salman Khalifah, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvi, Farrukh Jawad, Alvis-Guzman, Nelson, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Aly, Safwat, Alzoubi, Karem H, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Amini-Rarani, Mostafa, Amiri, Sohrab, Ampomah, Irene Gyamfuah, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderlini, Deanna, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Anil, Abhishek, Anil, Sneha, Ansar, Adnan, Ansari-Moghaddam, Alireza, Antony, Catherine M, Antriyandarti, Ernoiz, Anvari, Saeid, ANWAR, 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Elsayed, Sheikh, Aziz, Sheikhi, Rahim Ali, Shenoy, Manjunath Mala, Shetty, Pavanchand H, Shi, Peilin, Shiferaw, Desalegn, Shigematsu, Mika, Shiri, Rahman, Shirkoohi, Reza, Shittu, Aminu, Shivarov, Velizar, Shokraneh, Farhad, Shool, Sina, Shorofi, Seyed Afshin, Shuja, Kanwar Hamza, Shuval, Kerem, Siddig, Emmanuel Edwar, Silva, João Pedro, Silva, Luís Manuel Lopes Rodrigues, Silva, Soraia, Simonetti, Biagio, Singal, Anjali, Singh, Abhinav, Singh, Balbir Bagicha, Singh, Jasvinder A, Siraj, Md Shahjahan, Smith, Georgia, Socea, Bogdan, Sokhan, Anton, Solanki, Ranjan, Solanki, Shipra, Soleimani, Hamidreza, Soliman, Sameh S M, Solomon, Yonatan, Song, Yimeng, Sorensen, Reed J D, Spartalis, Michael, Sreeramareddy, Chandrashekhar T, Srivastava, Vijay Kumar, Stanikzai, Muhammad Haroon, Starodubov, Vladimir I, Starodubova, Antonina V, Stefan, Simona Cătălina, Steiropoulos, Paschalis, Stokes, Mark A, Subramaniyan, Vetriselvan, Suleman, Muhammad, Suliankatchi Abdulkader, Rizwan, Sultana, Abida, Sun, Jing, Swain, Chandan Kumar, Sykes, Bryan L, Szarpak, Lukasz, Szeto, Mindy D, Szócska, Miklós, Tabaee Damavandi, Payam, Tabarés-Seisdedos, Rafael, Tabatabaei Malazy, Ozra, Tabatabaeizadeh, Seyed-Amir, Tabatabai, Shima, Tabb, Karen M, Tabish, Mohammad, Taheri Soodejani, Moslem, Taiba, Jabeen, Tajbakhsh, Ardeshir, Talaat, Iman M, Talukder, Ashis, Tampa, Mircea, Tamuzi, Jacques Lukenze, Tan, Ker-Kan, Tang, Haosu, Tareke, Derbie Alemu DA, Tariku, Mengistie Kassahun, Tat, Vivian Y, Tavangar, Seyed Mohammad, Teimoori, Mojtaba, Temsah, Mohamad-Hani, Temsah, Reem Mohamad Hani, Teramoto, Masayuki, Terefa, Dufera Rikitu, Tesler, Riki, Teye-Kwadjo, Enoch, Thakur, Ramna, Thangaraju, Pugazhenthan, Thankappan, Kavumpurathu Raman, Thapar, Rekha, Tharwat, Samar, Thayakaran, Rasiah, Thomas, Nihal, Tichopad, Ales, Ticoalu, Jansje Henny Vera, Tiruye, Tenaw Yimer, Titova, Mariya Vladimirovna, Tonelli, Marcello, Tovani-Palone, Marcos Roberto, Traini, Eugenio, Tran, Jasmine T, Tran, Nghia Minh, Trihandini, Indang, Tromans, Samuel Joseph, Truyen, Thien Tan Tri Tai, Tsatsakis, Aristidis, Tsermpini, Evangelia Eirini, Tumurkhuu, Munkhtuya, Tyrovolas, Stefanos, Uddin, Sayed Mohammad Nazim, Udoakang, Aniefiok John, Udoh, Arit, Ullah, Atta, Ullah, Saeed, Ullah, Sana, Umakanthan, Srikanth, Umeokonkwo, Chukwuma David, Unim, Brigid, Unnikrishnan, Bhaskaran, Upadhyay, Era, Usman, Jibrin Sammani, Vacante, Marco, Vahabi, Seyed Mohammad, Vaithinathan, Asokan Govindaraj, Valizadeh, Rohollah, Van den Eynde, Jef, Varavikova, Elena, Varga, Orsolya, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Vellingiri, Balachandar, Venugopal, Deneshkumar, Verghese, Nicholas Alexander, Verma, Madhur, Veroux, Massimiliano, Verras, Georgios-Ioannis, Vervoort, Dominique, Villafañe, Jorge Hugo, Vinayak, Manish, Violante, Francesco S, Vishwakarma, Mukesh, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vo, Bay, Volovat, Simona Ruxandra, Vos, Theo, Vujcic, Isidora S, Wafa, Hatem A, Waheed, Yasir, Wakwoya, Elias Bekele, Wang, Cong, Wang, Denny, Wang, Fang, Wang, Shu, Wang, Yanzhong, Wang, Yuan-Pang, Ward, Paul, Wassie, Emebet Gashaw, Watson, Stefanie, Weaver, Marcia R, Weerakoon, Kosala Gayan, Weiss, Daniel J, Wells, Katherine M, Wen, Yi Feng, Westerman, Ronny, Wiangkham, Taweewat, Wickramasinghe, Dakshitha Praneeth, Wickramasinghe, Nuwan Darshana, Willeit, Peter, Wondimagegene, Yohannes Addisu, Wu, Felicia, Xia, Juan, Xiao, Hong, Xu, Gelin, Xu, Suowen, Xu, Xiaoyue, Yadollahpour, Ali, Yaghoobpoor, Shirin, Yaghoobpour, Tina, Yaghoubi, Sajad, Yahaya, Zwanden Sule, Yang, Danting, Yang, Lin, Yano, Yuichiro, Yaribeygi, Habib, Ye, Pengpeng, Yesodharan, Renjulal, Yesuf, Subah Abderehim, Yezli, Saber, Yigezu, Amanuel, Yip, Paul, Yon, Dong Keon, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z, Yousefi, Zabihollah, Yu, Chuanhua, Yu, Yong, Yuan, Chun-Wei, Zafari, Nima, Zakham, Fathiah, Zaki, Nazar, Zamagni, Giulia, Zandi, Milad, Zandieh, Ghazal G Z, Zangiabadian, Moein, Zastrozhin, Mikhail Sergeevich, Zhang, Haijun, Zhang, Meixin, Zhang, Yunquan, Zhong, Chenwen, Zhou, Juexiao, Zhu, Bin, Zhu, Lei, Zielińska, Magdalena, Zou, Zhiyong, Zyoud, Samer H, Murray, Christopher J L, Smith, Amanda E, and Vollset, Stein Emil

Published
2024
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32. Patrolling Robot with Facial Detection

Author

Golsangi, Mitrajeet, Ghuge, Vivek, Godse, Divija, Harne, Pravin, Thopate, Kaushalya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Dey, Nilanjan, editor, Perumal, Thinagaran, editor, and Tavares, João Manuel R. S., editor

Published
2023
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33. Tuberculosis: Integrated Studies for a Complex Disease 2050

Author

Rezaei, Nima, Hosseini, Nastaran-Sadat, Saghazadeh, Amene, Fateh, Abolfazl, Duse, Adriano, Ahmad, Aijaz, Braley, Alexander E., Tahta, Alican, Kamboj, Alisha, Khan, Amer Hayat, Coelho, Ana Cláudia, Fuso, Andrea, Varón, Andrés, Trajman, Anete, Saxena, Anil Kumar, Ganeshpurkar, Ankit, Casapao, Anthony M., Tkachenko, Anton, Devnikar, Anushka V., Moshiri, Arfa, Muñoz-Barrutia, Arrate, Dasgupta, Arunava, Natarajan, Arvind, Gupta, Ashish, Coyne, Ashlan J. Kunz, Jordan, Ashly E., Kumar, Ashok, Ergeshov, Atadzhan, Pourakbari, Babak, Joshi, Basant, Chavarro-Portillo, Bibiana, Soto, Carlos Y., Kanipe, Carly, Schmidt, Christiane Mello, Cox, Christophe, Gómez-Cruz, Clara, Cardoso, Claudete Aparecida Araújo, Sant´Anna, Clemax Couto, Johnson, Courtney, Rodríguez-Silva, Cristhian N., Rosales, Cristian, Licona-Cassani, Cuauhtémoc, Fast, Cynthia D., Pérez-Martínez, Damián, Pizzol, Damiano, Perlman, David C., Philips, Dennis, Viveros, Diana, Fisher, Dina A., Butov, Dmytro, Egelund, Eric F., de Igartua, Everest, Bhatt, Garima, Mgode, Georgies, Quaglio, Gianluca, Putoto, Giovanni, Mini, G. K., Thomas-Richardson, Govind, Wylie, Greg, dos Santos Fernandes, Guilherme Felipe, Bermúdez, Gustavo, Quintas, Hélder, Verma, Himanshu, Eoh, Hyungjin, Putera, Ikhwanuliman, Sivokozov, Ilya, Pires, Isabel, Lee, Jae Jin, Lombard, Jason E., dos Santos, Jean Leandro, Zellweger, Jean-Pierre, Thomas-Richardson, Jenu, Lordson, Jinbert, Prates, João Lucas, Cervantes, Jorge, Porcel, José M., Vaquero, Juan José, Prada, Justina, Kamboj, Kamal, Tabbara, Khalid F., Mussie, Kirubel Manyazewal, Patel, Krupesh, Porcel, Laura, Fiebig, Lena, Mohan, Malu, Yadav, Mange Ram, López-R, Marcela, Correia-Neves, Margarida, da Conceição Fontes, Maria, de Fátima Pombo Bazhuni Sant´Anna, Maria, Cañadas-Ortega, Marina, Singh, Meenakshi, Lause, Michael, Maya-Hoyos, Milena, Omrani, Mir Davood, Palmer, Mitchell V., Ahmad, Mohammad Naiyaz, Seid, Mohammed Assen, Chauhan, Monica, Saxena, Mridula, Marimani, Musa, Srinivas, Nanduri, Beyene, Negussie, Arenas, Nelson E., Cardoso, Nicole, Oliveira, Olena, Silakari, Om, Inlamea, Osvaldo, Tanrıverdi, Özgür, Boggiatto, Paola M., Santos, Paola, Mejía-Ponce, Paulina, Soares, Pedro, Sell, Philip, Murumkar, Prashant R., Devanandan, Praveen, Zheng, Qi, Lim, Rachel K., Aurílio, Rafaela Baroni, Ghuge, Rahul B., Barot, Rahul R., Rahul, Puvvada, Ranadheer Chowdary, Duarte, Raquel, Singh, Ravi, Sinha, Richa, La Distia Nora, Rina, Burny, Robert, Zenteno-Cuevas, Roberto, Mali, Sagar, Shoughy, Samir S., Tarashi, Samira, Mishra, Sapna, Malasala, Satyaveni, Mamishi, Setareh, Siadat, Seyed Davar, Choudhary, Shalki, Mahmoudi, Shima, Chopra, Sidharth, Nandi, Sisir, Faisal, Sobia, Goel, Sonu, Huszár, Stanislav, Field, Stephen K., Singh, Sushil Kumar, Rito, Teresa, Butova, Tetiana, Manning, Thomas, van der Werf, Tjip S., Myasoedov, Valeriy, Vásquez, Vanessa, Muthukumar, Vijey Aanandhi, Singh, Vinayak, Hall, Walter A., Hikmahwati, Wandya, Turgut, Yaşar Barış, Thaker, Yatri, Kawabata, Yoshinori, de Reus, Yvette A., and Rezaei, Nima, Editor-in-Chief

Published
2023
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35. Design and Development of Universal Runner Cutting Machine

Author

Waghmare, Gopaldas, Nemad, Atharva, Puri, Gaurav, Teware, Devyani, Banubakode, Aalapi, Ghuge, Shrinath, Howlett, Robert J., Series Editor, Littlewood, John, Series Editor, Jain, Lakhmi C., Series Editor, Bindhu, V., editor, Tavares, João Manuel R. S., editor, and Chen, Joy Iong-Zong, editor

Published
2023
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47. Quasi-Polynomial Algorithms for Submodular Tree Orienteering and Other Directed Network Design Problems

Author

Ghuge, Rohan and Nagarajan, Viswanath

Subjects
Computer Science - Data Structures and Algorithms
Abstract

We consider the following general network design problem on directed graphs. The input is an asymmetric metric $(V,c)$, root $r^{*}\in V$, monotone submodular function $f:2^V\rightarrow \mathbb{R}_+$ and budget $B$. The goal is to find an $r^{*}$-rooted arborescence $T$ of cost at most $B$ that maximizes $f(T)$. Our main result is a simple quasi-polynomial time $O(\frac{\log k}{\log\log k})$-approximation algorithm for this problem, where $k\le |V|$ is the number of vertices in an optimal solution. To the best of our knowledge, this is the first non-trivial approximation ratio for this problem. As a consequence we obtain an $O(\frac{\log^2 k}{\log\log k})$-approximation algorithm for directed (polymatroid) Steiner tree in quasi-polynomial time. We also extend our main result to a setting with additional length bounds at vertices, which leads to improved $O(\frac{\log^2 k}{\log\log k})$-approximation algorithms for the single-source buy-at-bulk and priority Steiner tree problems. For the usual directed Steiner tree problem, our result matches the best previous approximation ratio [GLL19]. Our algorithm has the advantage of being deterministic and faster: the runtime is $\exp(O(\log n\, \log^{1+\epsilon} k))$. For polymatroid Steiner tree and single-source buy-at-bulk, our result improves prior approximation ratios by a logarithmic factor. For directed priority Steiner tree, our result seems to be the first non-trivial approximation ratio. All our approximation ratios are tight (up to constant factors) for quasi-polynomial algorithms.

Published
2018

48. A comparative review on High-Performance Liquid Chromatography (HPLC), Ultra Performance Liquid Chromatography (UPLC) & High-Performance Thin Layer Chromatography (HPTLC) with current updates

Author

Gupta Manish Kumar, Ghuge Aditya, Parab Manasi, Al-Refaei Yehya, Khandare Anjali, Dand Neha, and Waghmare Nilkamal

Subjects
hplc, uplc, hptlc, comparative, Medicine
Abstract

Any chosen analytical method should be subtle, precise, fast and exact to begin the guarantee that the material used in the manufacturing is free of unsolicited impurity, the existence of which may vary the safety and effectiveness of the drug product. The techniques of HPLC and UPLC have established their part in pharmaceutical cleaning validation. High-Performance Liquid Chromatography (HPLC) is the main pharmaceutical and biomedical analysis approach utilized today because it generates highly efficient separations, and in most circumstances, it provides high detection sensitivity. Applying the HPLC method has several advantages compared to other methods, among others, specificity, rapidity, accuracy, precision, and the ease of automation. Due to the aforementioned, most drugs in a multi-component dosage form can be analyzed. Ultra-Performance Liquid Chromatography (UPLC) is a modern-day technique that gives a new track for liquid chromatography. UPLC provides the user with speed of application, resolution and sensitivity. The quantification and separation in UPLC are done under very high pressure (up to 100M Pa). High-Performance Thin Layer Chromatography (HPTLC) has improved and innovative separation efficacy and detection limits. It is a cultured and automated form of Thin Layer Chromatography (TLC) and is based on the use of an optimized silica gel 60 with a significantly smaller particle size than which is used for TLC. The previously stated analytical methods are employed for purity control of chemicals, steroids, pesticides, and water analysis, water-soluble food dyes, vitamins, pesticides in vegetables, fruits, and other foodstuffs. The current updates in the techniques allow us to understand the increased utilization of these methods in the current eras.

Published
2022
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