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A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting
The amount of rain received in a particular demographic region in a given time interval is called the rainfall. Rainfall is a natural and complex process and has significance in different domains including agriculture, transport, disaster management, and natural calamities resilience [1]. Abnormal rainfall affects every facet of humans and all other living beings of the world and also has a great impact in wellbeing and financial disruptions of a country. Accurate rainfall predictions at regular time intervals are always important to issue warnings about likelihood of any disaster about to happen. This also provides people a time for strategic planning in their work and precautions at time of adversity [2]. It is worth noting that rainfall forecasting does not only have an impact in day-to-day life, but more importantly for tropical countries like India where the chief occupation being agriculture and also for various other industries. It largely helps in disaster management and recovery process as well. The rainfall being a variable over time, geography and atmospheric conditions makes the forecasting considerably difficult [3]. Rainfall forecasting keeps a person informed about the likelihood of rainfall the forthcoming day, week, or month which enable long-time planning and on the other way; hourly prediction helps for shortterm planning such as enforcing traffic measures. Literature has seen various studies in this domain using predictive machine learning (ML) algorithms such as neural networks (NNs), Genetic algorithms, and Fuzzy-based systems [4]. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
A multilevel analysis of hiv1-miR-H1 miRNA using KPCA, K-means, Random Forest and online target tools
The goal of this study was to propose a workflow using machine learning to identify and predict the miRNA targets of Human Immunodeficiency virus 1. miRNAs which is ~21 nt long are attained from larger hairpin RNA precursors and is maintained in the secondary structure of their precursor relatively than in primary chain of successions. The proposition approach for identification and prediction of miRNA targets in hiv1-miR-H1is based on secondary structure and E-value through machine learning. Data Linearity of Length and e-value for sequence match with hiv1-mir-H1 is verified using Kernel PCA. miRNA targets were grouped into clusters thereby indicating similar targets using K-means algorithm. Classification model using Random Forest was implemented regards to each secondary features variable considering feature relevance. A learning methodology is put forward that assimilate and integrate the score returned by various machine learning algorithms to predict cellular hiv1-miR-H1 targets. Gene targets results using TargetScan, miRanda, PITA, DIANA microT and RNAhybrid are also explored for multiple parameters. 2021 Inderscience Enterprises Ltd. -
A Multiplier-Less FRM-Based Reconfigurable Regulated Bank of Filter for Spectrum Hole Detection in IoT
A promising solution for the detection of spectrum holes in the Internet of Things networks is the cognitive radio (CR) system, which is used to identify spectrum holes effectively. The intention of this work is to design a low-complexity Reconfigurable Regulated Bank of Filter (RRBF) structure for spectrum hole detection in IoT networks. The RRBF structure is designed by utilizing the Frequency Response Masking (FRM) approach and the Cosine Modulation Technique (CMT). Using the RRBF structure, multiple sharp non-uniform channels are generated for efficient spectrum hole detection in IoT networks. With the aid of an example, the performance and computational complexity of the RRBF structure are demonstrated. The result shows that the RRBF structure has a fewer multipliers than other existing methods. To obtain hardware-efficient realization, the RRBF structure is made of multiplier-less by incorporating Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with Common Sub-expression Elimination (CSE) optimization techniques. 2024 IETE. -
A Narrative Review on Experience and Expression of Anger Among Infertile Women
Infertility is stressful among women though there are several technological advancements in treating infertility. Anger is a powerful emotion resulting due to stigma and oppression due to infertility, especially among women. Studies have also proven that women have a poor quality of life in the context of infertility. Women are prone to suppressing anger rather than dealing with anger in the present. Psychosocial intervention and psychoeducation help women manage anger and maintain healthy quality of life. Springer Nature Switzerland AG 2023. -
A Narrative Synthesis on the Role of Affective Computing in Fostering Workplace Well-Being Using a Deep Learning Model
Emotional information is more valued in the modern workplaces with increased focus on the need for sensing, recognizing and responding to human emotions. Integrating human emotions as information for communication and decision-making is possible through the computer-based solution called as affective computing. Affective computing is a relatively less explored AI platform though the notion is more than two decades old. The cognitive algorithms employed in affective computing operates in three key areas, viz. context sensitivity, augmented reality, and proactiveness, with outcomes in the fields of emotion management, health, and productivity. Affective computing promises better management of organizational outcomes such as fostering workplace well-being, promoting happiness, productivity, engagement levels, and communication. Further, affective computing can play vital roles in an employees life cycle with applications in functional areas of HRM like employee selection, training and development, and performance management. Even as workplaces are increasingly adopting affective computing, an analysis of its positive effects can help practitioners take informed decisions about its implementation. This paper outlines the theoretical underpinnings of affective computing, discusses the relevance of ResNet50 in image analysis, and proposes a step-by-step methodology for implementing affective computing techniques in the workplace. The potential benefits and challenges of adopting affective computing in fostering workplace well-being are also discussed. Thus, this chapter investigates the role of affective computing in fostering well-being in the workplace usinga deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Neural Network Based Customer Churn Prediction Algorithm for Telecom Sector
For telecommunication service providers, a key method for decreasing costs and making revenue is to focus on retaining existing subscribers rather than obtaining new customers. To support this strategy, it is significant to understand customer concerns as early as possible to avoid churn. When customers switch to another competitive service provider, it results in the instant loss of business. This work focuses on building a classification model for predicting customer churn. Four different deep learning models are designed by applying different activation functions on different layers for classifying the customers into two different categories. A comparison of the performance of the different models is done by using various performance measures such as accuracy, precision, recall, and area under the curve (AUC) to determine the best activation function for the model among tanh, ReLU, ELU, and SELU. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN
Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested methods purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs. 2022, Tech Science Press. All rights reserved. -
A new algorithm with its randomness and effectiveness against statistical tests in data encryption
In the world where security is one of the main concern, we are still not able to make our data secure. Privacy is one of the major concerns in todays world, where all the organization are dealing with data leak problem, data theft, data intrusion. We came up with a mathematical model to encrypt and decrypt data securely. In this paper we have came up with a technique to encrypt and decrypt data using non-deterministic random numbers and generating two cipher text for each data unit (character) and verified the randomness of our cipher text using chi-square test, Gaps test. IJSTR 2020. -
A New Approach to Robust Weighted Support Vector Regression and Its Applications in Medical MRI Image Processing
In recent years, the field of machine learning has experienced significant growth, with the emergence of various advanced technologies leveraging its principles. Among these, Support Vector Regression (SVR) has established itself as a widely recognized and robust regression technique. This article introduces a novel approach, Robust Hampel Weight-Based Support Vector Regression (RH-SVR), designed to enhance the resilience and efficiency of traditional SVR. The study investigates and compares several regression methods, including the Robust Linear Model (RLM), SVR, RH-SVR, and Least Squares Regression (LS). An experimental analysis was conducted using MRI images of the human heart and brain, both in their original form and with added noise at varying levels (10, 20, and 30%). Performance metrics such as Mean Square Error (MSE), Median Absolute Error (MDAE), Relative Standard Error (RSE), and Peak Signal-to-Noise Ratio (PSNR) were evaluated. The results consistently demonstrate that the proposed RH-SVR method achieves lower error rates and higher PSNR values, showcasing superior accuracy and robustness, particularly when processing noisy images. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A new assessment of quantum key distribution, attenuation and data loss over foggy, misty and humid environment
Quantum encryption is a method of key transfer in cryptography by using quantum entanglement of photons. The real power of quantum entanglement is instantaneous communication that is non intercept able. The advantage of quantum encryption method is, it can be incorporated with conventional encryption methods safely. The quantum cryptography can replace conventional key exchange mechanism with the polarized photons using channels like optic fiber cables. Quantum cryptographic can also provide far and secure data communication. The present day experiments clearly proved that the quantum cryptography can be implemented through medium like optic fiber cable or air. But the distance of transmission through the air is limited by rule of line of sight propagation. The quantum key distribution will have uses in different types of communication between distant parts of earth. So this paper discussing various aspects of Quantum key distribution and successfully calculated polarized photon loss during transmission of Quantum cryptography link, while using in various type of atmospheric conditions like Mist Fog Haze. Also successfully calculated probability of single polarized photon missing by successfully utilizing the Light transmission characteristics and power measurements in various Atmospheric conditions. 2018, UK Simulation Society. All rights reserved. -
A new benzothiazoloacetonitrile-derived fluorescent probe for selective hydrazine detection and its applications in bioimaging and cotton swab analysis
Hydrazine (N2H4) is extensively utilized in various chemical industries. However, it is a highly toxic and explosive chemical posing a serious risk to human health and the environment, which warrants its quick and selective detection. To address this issue, we introduce a benzothiazoloacetonitrile-based fluorescent probe containing a recognition site for hydrazine detection. Adding the benzothiazoloacetonitrile group to the phenanthroline-based imidazole fluorophore increased BTN's electrophilicity, aiding the nucleophilic attack by hydrazine. This led to a rapid fluorescence change from orange to green within one minute, with a limit of detection (LOD) of 0.21 M, resulting from the cleavage of the olefinic bond between the donor and acceptor units. The probe's selective response to hydrazine was supported by a specific reaction mechanism, confirmed by LC-MS and DFT studies. Additionally, the probe can detect hydrazine using cotton swabs for quick, on-site testing. It also allows for clear visualisation in living cells through different fluorescence channels. Overall, these results demonstrate that the probe exhibits significant potential for the detection of hydrazine in environmental and biological samples. This journal is The Royal Society of Chemistry, 2026. -
A new broad-band atmospheric dispersion corrector for HROS-TMT
Atmospheric dispersion causes light from celestial objects with different wavelengths to refract at varying angles as it passes through Earths atmosphere. This effect results in an elongated image at the focal plane of a telescope and diminishes fibre coupling efficiency into spectrographs. We propose an optical design that incorporates a Rotational Atmospheric Dispersion Corrector (RADC) to address the broad-band dispersion for the multi-object mode of the High-Resolution Optical Spectrograph (HROS) on the Thirty Meter Telescope (TMT). The RADC corrects the dispersion across the entire wavelength range (0.311 ?m), using Amici prisms optimized for over 90 per cent transmission efficiency and minimal angular deviation of the beam from the optical axis after dispersion correction. For enhanced accuracy, particularly in the blue region, we have, for the first time, implemented the Filippenko model in Zemax via a custom Dynamic-Link Library (DLL) file. The Author(s) 2025. Published by Oxford University Press on behalf of Royal Astronomical Society. -
A new combinational technique in image steganography
Internet is used for exchanging information. Sometimes it is needed to transmit confidential data via internet. Here the authors use image steganography to pass confidential data within a cover image. To construct the algorithm, they take the combinational help of particle swarm optimization (PSO), bi-orthogonal wavelet transform (BWT), and genetic algorithm (GA). They use PSO to take the enhanced version of cover image. They use BWT to choose the selective sub bands of cover image and we utilize GA to select a particular stego image among a set of stego images. Thus, an innovative technique of image steganography has been made to transmit confidential data via cover image generating stego image. This combinational approach of image steganography is quite safe for confidential data transmission and makes it hard for the attackers to retrieve the confidential data. 2021 IGI Global. All rights reserved. -
A new computational technique for the analytic treatment of time-fractional EmdenFowler equations
This paper presents the study of fractional EmdenFowler (FEF) equations by utilizinga new adequate procedure, specifically the q-homotopy analysis transform method (q-HATM). The EF equation has got greater significance in both physical and mathematical investigation of capillary and nonlinear dispersive gravity waves. The projected technique is tested by considering four illustrations of the time-fractional EF equations. The q-HATM furnish ?, known as an auxiliary parameter, by the support of ? we can modulate the various stages of convergence of the series solution. Additionally, to certify the resolution and accurateness of the proposed method we fitted the suitable numerical simulations. The redeem results guarantee that the proposed process is more convincing and scrutinizes the extremely nonlinear issues emerging in the field of science and engineering. 2021 International Association for Mathematics and Computers in Simulation (IMACS) -
A New Economics Awaits Us
This article attempts to look into the concept of othering in the context of urban development. The major motivation for the initiation of this article came after reading Dipankar Guptas book review titled A New Sociology Awaits Us (EPW, 26 December 2020), which mainly concentrated on urban affairs. 2022 Economic and Political Weekly. All rights reserved. -
A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process
Wind energy is an energy source that is naturally clean, safe and cheap. It comes from a variety of sources. The electric energy generated by a wind turbine manifests as kinetic energy throughout the earth. The energy received from the wind is clean and is permanently available and can be generated forever. Turbine characteristics also have an impact on wind energy production. The turbine properties within a wind farm are important in estimating the load on power generation and wind turbine energy. The amount of energy released is calculated according to the type of the turbine model applied. In many situations, the choices of turbine model can incur various vague and complicated hesitation situations. To manage this situation, a hesitant fuzzy set with the Multi Criteria Decision Making (MCDM) is used. In the present research, the newly proposed Normal Wiggly Hesitant Fuzzy-Criteria Importance Through Intercriteria Correlation (NWHF-CRITIC) and Normal Wiggly Hesitant Fuzzy-Multi Attribute Utility Theory (NWHF-MAUT) methods were employed to rank turbine models based on quality, power level, voltage, and capacity. As part of this process, the NWHF method was utilized to extract and gather deeper information from the decision-makers. 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. -
A New Facile Iodine-Promoted One-Pot Synthesis of Dihydroquinazolinone Compounds
A one-pot iodine catalyzed reaction has been developed for the preparation of dihydroquinazolinones from isatoic anhydride, enaminones, and amines in modest to good yields. The reaction has been screened in various catalysts and solvents and a gram scale experiment has been performed based on the optimum conditions. A possible mechanism has been proposed based on the control experiments. The reaction has been checked with broad range of substrates. 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
A new facile synthesis of (2S,5S)-5-hydroxypipecolic acid hydrochloride
A simple and efficient synthesis of (2S,5S)-5-Hydroxypipecolic acid hydrochloride is reported. The key features of the synthesis involve the asymmetric reduction of ketone using (S)-CBS oxazaborolidine and the use of commercially available methyl pyroglutamate as a starting material.. 2022 Taylor & Francis Group, LLC. -
A New Facile Ultrasound-Assisted Magnetic Nano-[CoFe2O4]-Catalyzed One-Pot Synthesis of Pyrano[2,3-c]pyrazoles
Pyrano[2,3-c]pyrazole derivatives have been synthesized through a one-pot multicomponent condensation of various aldehydes, dialdehydes, and ketones with malononitrile, ethyl acetoacetate, hydrazine hydrate (or phenylhydrazine) in the presence of magnetic nano-[CoFe2O4] catalyst under ultrasonic irradiation. The catalyst can be recovered using an external magnet and used repeatedly. 2019, Pleiades Publishing, Ltd. -
A new framework for contour tracing using Euclidean distance mapping
In this paper, a new fast, efficient and accurate contour extraction method, using eight sequential Euclidean distance map and connectivity criteria based on maximal disk, is proposed. The connectivity criterion is based on a set of point pairs along the image boundary pixels. The proposed algorithm generates a contour of an image with less number of iterations compared to many of the existing methods. The performance of the proposed algorithm is tested with a database of handwritten character images. In comparison to two standard contour tracing algorithms (the Moore method and the Canny edge detection method), the proposed algorithm found to give good quality contour images and require less computing time. Further, features extracted from contours of handwritten character images, generated using the proposed algorithm, resulted in better recognition accuracy. Copyright 2021 Inderscience Enterprises Ltd.