Ankita Nandy

Bio: Ankita Nandy is an academic researcher from University of Calcutta. The author has contributed to research in topics: Coin problem & Counterfeit. The author has an hindex of 1, co-authored 4 publications receiving 3 citations.

An algorithm for identifying two unequal heavier / lighter coins out of n given coins

Abstract: Counterfeit coin problem has been considered for a very long time and is a topic of great significance in Mathematics as well as in Computer Science. In this problem, out of« given coins, two or more false coins (the coins are classified as false because their weights are different when compared to a standard coin) are present which have the same appearance as the other coins. This problem belongs to the class of combinatorial group testing problem which finds several applications in hidden graph construction problem etc. In this paper, we have constructed an optimal algorithm to determine two false coins out of a given number of coins. In addition, our objective is to solve the problem in minimum number of comparisons with the help of an equal arm balance. Our proposed algorithm is able to find out the fake coins using O(log n) comparisons.

An endeavour to find two unequal false coins

Abstract: The counterfeit coin problem is well-known and truly interesting in Computer Science, Game theory, and also in Mathematics In this problem the objective is to detect the fake coin(s) of identical appearance but different weight in minimum number of comparisons The word counterfeit most frequently describes forgeries of currency or documents, but can also describe software, pharmaceuticals, clothing, and more recently, motorcycles and cars, especially when these result in patent or trademark infringement Finding one fake coin among n coins is tricky enough and complex The problem becomes rigorous when there are two fake coins, as the false coin pair may form several different combinations that make the problem particularly tricky and complex to solve In this paper we have developed a new algorithm for solving two versions of the two counterfeit coins problem in O(log n) time, where n is the number of coins given

The first algorithm for solving two coins counterfeiting with ω(ΔH) = ω(ΔL)

Abstract: Counterfeit coin problem is of utmost importance and it is truly interesting in Computer Science and Game theory as well as in Mathematics In this problem the objective is to detect the fake coin(s) of identical appearance but of different weight in minimum number of comparisons The word counterfeit is most frequently applicable to forgeries of currency or documents, but can also describe software, pharmaceuticals, clothing, and more recently, motorcycles and other vehicles, especially when these result in patent or trademark infringement In this paper we have developed a new algorithm for solving two counterfeit coins problem in linear time, where n is the total number of coins given However, this is the first algorithm that identifies and solves the problem, given the false coins with type ω(ΔH) = ω(ΔL), ie, one false coin is heavier and another is lighter than a true coin, and their difference in weight from the true coin is equal However, this is the degenerate case in the field of two counterfeit coins problem

Anomaly Detection and Three Anomalous Coins Problem

Abstract: Counterfeit coin problem has been considered for a very long time and is a topic of great significance in Mathematics as well as in Computer Science. In this problem, out of n given coins, one or more false coins (the coins are classified as false because of their different weight from a standard coin) are present which have the same appearance as the other coins. The word counterfeit or anomalous means something deviated from the standard one. In this respect, finding out these anomalous objects from a given set of data items is of utmost importance in data learning problem. Thus, representing coins as any data items, we have introduced an algorithm to determine three false coins out of n given coins. In addition, our objective is to solve the problem in minimum number of comparisons with the help of an equal arm balance.

Forecasting the Movement of Renewables Stocks Using BSE Energy Index1

Abstract: Coincident to the dip in the demand of conventional sources of energy like coal, oil and gas as the pandemic progressed has been a surge in the global demand for environment friendly practices, putting the spotlight on energy generated from renewable sources. The Renewables sector has found favor and is witnessing steady rise on a global level. Though a minor contributor to the power generation in India, this sector is deemed to grow in the coming years as India strives to reduce its CO2 emissions, making the related instruments lucrative investment options. Stock exchanges are critical to the economic health of a nation and the pandemic led to major crashes in several exchanges around the world. Investment firms can employ deep learning models to forecast the movement of the market and thus assure their customers of high returns in the high-risk environment, cutting through the general pessimism pervading the investment sphere post-pandemic. This work builds forecasting models for two such stocks using neural networks. Selecting the BSE as the universe of study, two companies are selected and modelled across two techniques: LSTM and Bidirectional LSTM, employing three different feature sets. The inclusion of BSE Energy Index in the models alongside the historical prices enables capturing the influence of external elements on the energy market.

Anomaly Detection and Three Anomalous Coins Problem

Abstract: Counterfeit coin problem has been considered for a very long time and is a topic of great significance in Mathematics as well as in Computer Science. In this problem, out of n given coins, one or more false coins (the coins are classified as false because of their different weight from a standard coin) are present which have the same appearance as the other coins. The word counterfeit or anomalous means something deviated from the standard one. In this respect, finding out these anomalous objects from a given set of data items is of utmost importance in data learning problem. Thus, representing coins as any data items, we have introduced an algorithm to determine three false coins out of n given coins. In addition, our objective is to solve the problem in minimum number of comparisons with the help of an equal arm balance.