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Deprotection induced modulation of excited state intramolecular proton transfer for selective detection of perborate and ammonia
Acetate protected Naphthalene Coupled Benzothiazole (NCB) has been designed and synthesized for selective detection of perborate (BO3) and ammonia (NH3) based on modulation of excited-state intramolecular proton transfer (ESIPT) process by chemodosimetric deacetylation pathway. In presence of nucleophilic species like BO3 and NH3, acetyl group deprotection of NCB resulted ESIPT within the molecule exhibiting a significant enhancement of absorption and emission signals at 425 nm and 472 nm respectively. The emission enhancement of NCB has been observed by 31-folds and 14-folds in presence of BO3 and NH3 respectively. The selectivity and fast sensitivity of NCB have been shown by the lower detection limit (1.32 M for BO3, 1.74 M for NH3 in UVvis study and 0.60 M for BO3 and 4.39 M for NH3 in fluorescence study) and fast response (rate constants: 12.36 s?1 and 5.54 s?1 for BO3 and NH3 respectively). Analytes induced deacetylation pathway of NCB followed by ESIPT has been clearly demonstrated by theoretical calculation. The test strips based on NCB with BO3 and NH3 are fabricated, which can act as a convenient and efficient test kits for both these analytes. In the practical applications, the sensor NCB can be utilized as low cost food spoilage indicator and soil analysis by fluorometric method. 2024 Elsevier B.V. -
Vehicular Propagation Velocity Forecasting Using Open CV
This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety. 2023 IEEE. -
A smart attendance system and method for permission inventory during the class /
Patent Number: 202111060922, Applicant: Shivani Chaudhry.
A smart attendance system (1). The system (1) comprises a smart lecture stand (2), which having an electronic unit (2A) which is connected to the other smart door, smart bench, and smart chair of the system; a smart bench (3), which having an electronic unit (3A), which is connected to the other smart door, smart stand, and smart chair of the system; a smart chair (4) comprises which having an electronic unit (4A); which is connected to the other smart door, smart bench, and smart stand of the system; a smart door (5) comprises a electronic unit (5A), which is connected to the other smart door, smart bench, and smart chair of the system. -
Method of enhancing quality of services in cloud computing environment using load balancer /
Patent Number: 202211006218, Applicant: Dr. Pratibha Giri. -
Stock market prediction using artificial neural networks in python /
Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation). -
Credit card fraud detection using python and machine learning /
Patent Number: 202221047470. Applicant: Pankaj Shambunath Mishra.
Credit Card Fraud Detection Using Python and Machine Learning Abstract: As we are heading towards the digital world cyber security is becoming an essential element of our life. When we talk about security in digital life then the major challenge is to discover the anomalous activities. When we make any transaction while purchasing any product online a big proportion of consumers choose credit cards. The credit limit in credit cards often helps us to making purchases even if we don’t have the funds at that time. But, on the other hand, these features are utilised by cyber attackers. -
A smart attendance system and method for permission inventory during the class /
Patent Number: 202111060922, Applicant: Shivani Chaudhry.
A smart attendance system (1). The system (1) comprises a smart lecture stand (2), which having an electronic unit (2A) which is connected to the other smart door, smart bench, and smart chair of the system; a smart bench (3), which having an electronic unit (3A), which is connected to the other smart door, smart stand, and smart chair of the system; a smart chair (4) comprises which having an electronic unit (4A); which is connected to the other smart door, smart bench, and smart stand of the system; a smart door (5) comprises a electronic unit (5A), which is connected to the other smart door, smart bench, and smart chair of the system. -
Method of enhancing quality of services in cloud computing environment using load balancer /
Patent Number: 202211006218, Applicant: Dr. Pratibha Giri. -
Stock market prediction using artificial neural networks in python /
Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation). -
Credit card fraud detection using python and machine learning /
Patent Number: 202221047470. Applicant: Pankaj Shambunath Mishra.
Credit Card Fraud Detection Using Python and Machine Learning Abstract: As we are heading towards the digital world cyber security is becoming an essential element of our life. When we talk about security in digital life then the major challenge is to discover the anomalous activities. When we make any transaction while purchasing any product online a big proportion of consumers choose credit cards. The credit limit in credit cards often helps us to making purchases even if we don’t have the funds at that time. But, on the other hand, these features are utilised by cyber attackers. -
Artificial intelligence and deep learning based driverless cars to reduce the road accident, death rate using python /
Patent Number: 202221047470, Applicant: Rashel Sarkar.
2% of global deaths each year are caused by automobile accidents. This corresponds to around 3,287 each day, or 1,300,000 per year. 20 million to 50 million people are seriously injured in automobile accidents annually. Why do these recurring problems persist People do make errors. One careless or foolish action is all it takes to transform a safe drive into one that could kill someone. This holds true regardless of whether the driver is preoccupied, intoxicated, or simply careless or irresponsible. In terms of technology, Artificial Intelligence (AI) has always been ahead of the curve. -
Exact prediction and consumption of residential electricity power cost hours, daily, weekly, monthly using ant, ML and DL techniques /
Patent Number: 202241055650, Applicant: Dr. S Perumal.
This research describes an unique method for predicting energy consumption based on deep neural networks that can accurately estimate the hourly energy consumption profile of a residential building one day in advance, taking occupancy into account. Providers of energy and utilities can determine the most efficient generation schedule if they have an accurate evaluation of the quantity of energy utilised by houses. A comprehensive review of a number of criteria was undertaken in order to initiate an investigation into the various energy estimation techniques that employ machine learning. -
Evaluating the performance of machine learning using feature selection methods on dengue dataset
Dengue fever is a mosquito-borne disease transmitted by the bite of an Aedes mosquito infected with a dengue virus. The bites of an infected female Aedes mosquito which gets the virus while feeding on the infected persons blood, transmits the virus to others. Dengue transmission is climate sensitive for several reasons such as temperature, humidity, rainfall, etc. Areas having higher vapor pressure and rainfall rate are most vulnerable to the spreading of the dengue disease. So to find the important features responsible for spreading the dengue we have used the classification algorithms. Machine learning is one of the key methods used in modern day analysis. Many algorithms have been used for medical purposes. Dengue disease is one of the serious contagious diseases. To find the features related to spreading of dengue disease, we have used popular machine learning algorithms. This proposed work focuses on evaluating the performances of the various machine learning techniques like-Random Forest Classifier (RFC), Decision Tree Classifier (DTC) and Linear Support Vector Machine (LSVM). Predictive Mean Matching is applied for preprocessing of the data and percentage split is applied for resampling of the data. Information gain values for each of the attributes are calculated. The attributes are sorted on the basis of information gain values. Feature selection methods (FSMs) such as Forward Selection (FS) and Backward Elimination (BE) are applied to choose the finest subset of the attributes, so that the algorithm runs more efficiently with a lower run time. It also results in the improvement of the accuracy. The attributes selected by the Feature Selection Methods are the main attributes which results in the probable effects of global weather change on human healthiness. BEIESP. -
The changing paradigm - Gender dimensions of watershed management in Hosadurga Taluk, Chitradurga District, Karnataka, India /
Intenational Journal Of Science And Research, Vol.4, Issue 7, pp.280-285, ISSN No: 2319-7064 (Online). -
Magnetic property applications of microwave method prepared zinc ion modified CoAl2O4 nanoparticles
Employing Microwave combustion technique and utilizing L-arginine as fuel pure Cobalt Aluminate and Zn doped Cobalt Aluminate nanoparticles (NPs) were prepared. XRD, DRS-UV, HRSEM and VSM techniques were used to investigate the structural, optical, morphological, and magnetic properties. The average crystallite size is found in the range of 15-24 nm. Elemental confirmation is done by aid of EDX spectra. The band gap values of the produced samples were discovered to be between 2.57 and 2.45 eV. At room temperature, the prepared samples showed diamagnetic magnetic characteristics, which were corroborated by MagnetizationField (MH) hysteresis curves. 2021, S.C. Virtual Company of Phisics S.R.L. All rights reserved. -
Network Lifetime Enhancement by Elimination of Spatially and Temporally Correlated RFID Surveillance Data in WSNs
In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays. 2022 Lucy Dash et al. -
Recent developments in bandwidth improvement of dielectric resonator antennas
This article shows a compressed chronological overview of dielectric resonator antennas (DRAs) emphasizing the developments targeting to bandwidth performance characteristics in last three and half decades. The research articles available in open literature give strong information about the innovation and rapid developments of DRAs since 1980s. The sole intention of this review article is to, (a) highlight the novel researchers and to analyze their effective and innovative research carried out on DRA for the furtherance of its performance in terms of only bandwidth and bandwidth with other characteristics, (b) give a practical prediction of future of DRA as per the past and current state-of-art condition, and (c) provide a conceptual support to the antenna modelers for further innovations as well as miniaturization of the existing ones. In addition some of the significant observations made during the review can be noted as follows; (a) hybrid shape DRAs with Sierpinski and Minkowski fractal DRAs seems comfortable in obtaining wideband as well as multiband, (b) combination of multiple resonant modes (preferably lower modes) can lead to wider impedance bandwidth, (c) at proper matching wider patch with slotted dielectric resonator can exhibit better bandwidth. 2019 Wiley Periodicals, Inc. -
Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy
Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Body mass index implications using data analysis in the soccer sports
Soccer is considered among the most popular sports in the world among the last few years. At the same time, it has become a prime target in developing countries like India and other Asian countries. As science and technology grow, we can see that sports also grow with science, and hence technology being used to determine the results sometime or sometimes it is used to grow the overall effect. This paper presents the attributes and the qualities which are necessary to develop in a player in order to play for the big-time leagues called Premier League, La Liga, Serie A, German Leagues and so on. Simple correlation and dependence techniques have been used in this paper in order to get proper relationship among the attributes. This paper also examines how the body mass index plays an effect on the presentation of soccer players with respect to their speed, increasing speed, work rate, aptitude moves and stamina. The point is likewise to discover the connection of the above credits concerning body mass index. As in universal exchange, football clubs can profit more in the event that they have practical experience in what they have or can make a similar bit of room to maneuver. In a universe of rare assets, clubs need to recognize what makes them effective and contribute in like manner. Springer Nature Singapore Pte Ltd 2021. -
Jesuit school teachers opinions on incorporating critical consciousness into digital citizenship education
The contemporary global landscape is undergoing swift transformations accelerated by information and digital technologies, which have given rise to a plethora of innovations that enhance human convenience, novel business models, and emerging new professional paths. However, if these technologies are used improperly, they can become dangerous to humanity. So digital citizenship is a kind of way forward to bring awareness among students and educators to use digital technologies appropriately and responsibly. But in classical digital citizenship issues, such as justice, equity, and accessibility, are not addressed. This study explores Jesuit secondary school teachers opinions on incorporating critical consciousness into digital citizenship and how that affects students attitudes towards building an equitable digital society. The researcher highlights the need to integrate critical consciousness into digital citizenship education through qualitative research study. 2024 Informa UK Limited, trading as Taylor & Francis Group.