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Synthesis, structural characterization, electrochemical and photocatalytic properties of vanadium complex anchored on reduced graphene oxide
In this work, vanadium complex anchored reduced graphene oxide (rGO-VO) was successfully synthesized by coordination interaction with phenyl azo salicylaldehyde (PAS) coupled trimethoxy silyl propanamine (TMSPA). The physicochemical and microscopic properties of rGO-VO were studied with different analytical techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, Scanning electron microscopy (SEM), and Transmission electron microscopy (TEM) which confirmed the synthesis of rGO-VO. The electrochemical studies of rGO-VO in glassy carbon electrode demonstrated high current density because of the amazing electrochemical properties of rGO. The photocatalytic studies of anchored rGO-VO and VO(acac)2 toward MB dye indicated that anchored rGO-VO with visible light irradiated MB was degraded fast as compared to VO(acac)2. 2021 Taylor & Francis Group, LLC. -
An Automated Deep Learning Model for Detecting Sarcastic Comments
The concept of Natural Language Processing is immensely vast with a wide range of fields in which ideas can be explored and innovations can be developed. An algorithm based on deep learning is used to detect sarcasm in text in this paper. It is usually only possible to detect sarcasm through speech and very rarely through text. 1.3 million comments from Reddit were analyzed, of which half were sarcastic and half were not, and then various deep learning models were applied, such as standard neural networks, CNNs, and LSTM RNNs. The best performing model was LSTM-RNNs, followed by CNNs, and standard neural networks came last. With textual data, it is much harder to understand whether the other person is being sarcastic or not, it can only be understood by listening to their tone of voice or looking at their behaviour. The purpose of this paper is to demonstrate how to detect sarcasm in textual data using deep learning models. 2021 IEEE. -
Impact of demand response contracts on short-term load forecasting in smart grid using SVR optimized by GA
In a Smart Grid environment the performance measure of the grid is calculated by considering the fact that how accurately and precisely a load forecasting (LF) is done. A true Load Forecasting is vital to make a current grid smarter and more reliable when it comes to its performance. Demand Response (DR) contracts is a type of program in smart grid where the customer is free to select a type of contract which is given by the utility and is one of the growing factor which affects the load forecasting results in the Smart Grid, therefore in order to do a complete evaluation of smart grid performance and to accomplish an accurate load forecasting results the different types of contracts should also be studied. The purpose of this study is to accomplish two goals. The first one is to develop a suitable model which can incorporate various factors that can affect the load forecasting results. The subsequent goal is to identify the impact of the demand response contracts on the load forecasting results. In the proposed study, Support Vector Machine-Regression (SVR) is selected as the base methodology to perform a Short - Term Load Forecasting (STLF) under smart grid environment. 2017 IEEE. -
Price Discovery of Currency Futures at NSE
The current study aimed to examine the causal relationship between the NSE currency future rates and currency spot rates in order to identify the price discovery mechanism at NSE market and its integration with foreign exchange market (spot market). To study the causal relationship between the said markets, we have considered daily closing rates for NSE currency futures and currency spot rates for selected pairs of currencies, i.e. USD/INR, GBP/INR, JPY/INR and EURO/INR. The data was obtained from www.nseindia.com and www.investing.com for the period from Jan-2010 to Sep-2017, which makes approximately 1750 observations for each currency pair in each market. It is found that the spot rate for JPY/INR leads the future rate. It is also identified that the spot rate for USD/INR does not cause the changes in futures. It indicates that the market integration between spot and futures at NSE for currency pair USD/INR is strong compared to other selected currency pairs. From the variance decomposition test we found that there is almost no impact of variance in USD/INR spot rate on future rate variance forecast errors. It implies that the causal relationship between for USD/INR spot and future rates is strong and mature compared to the measured causal relationships for the remaining currency pairs. This study concludes that the price discovery process for currency pair USD/INR is better at NSE currency futures among the selected currency pairs. Copyright 2022 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License -
Emerging ternary nanocomposite of rGO draped palladium oxide/polypyrrole for high performance supercapacitors
In this work, novel electrodeposited palladium oxide-polypyrrole (PdP) and its ternary composite with reduced graphene oxide (PdPGO) draped over the surface of PdP were synthesised to achieve the excellent electrochemical properties and high stability. An exhaustive study has been carried out to correlate the crystalline structure, chemical bonding, morphological behaviour, redox reactions at the electroactive species, and its promising influences on the electrochemical performance. The electrodeposited PdPGO composite on stainless steel bestows superior electrochemical properties and a specific capacitance of 595 F g?1 at 1 A g?1 in 1 M H2SO4. The incorporation of rGO with the PdP matrix prevents the aggregation of rGO layers and is responsible for the enhanced electrostatic interactions at the electrode-electrolyte interface in PdPGO. Outstanding supercapacitance retention of 88% even after 5000 cycles at 5 A g?1 was accomplished for the ternary composite of Pd. These profound electrochemical characteristics are due to the synergistic effect of the individual components involved, manifest a great potential for Pd based composites toward novel electrode materials for supercapacitors of high efficiency. This method facilitates blueprints for synthesizing a series of advanced electrode materials for enhancing high storage capability. The high electrochemical performance of the PdPGO reveals how synergy plays a very important role to work on the blueprint to create active electrode materials for energy storage solutions. 2020 Elsevier B.V. -
Benzoyl hydrazine-anchored graphene oxide as supercapacitor electrodes
In this study, benzoyl-hydrazine anchored graphene oxide (BHGO) is synthesised using graphene oxide (GO) and benzoyl hydrazine (BH) via a simple, cost effective ultrasonic assisted chemical route. BH acted as a nitrogen source, reducing agent, and morphology modifier resulting in good electrochemical performance of BHGO. The supercapacitor behaviour of BHGO is investigated in different aqueous electrolytes and it exhibits a specific capacitance of 170 F g?1 at a current density of 1 A g?1 in 1 M H2SO4 and capacitive retention of 85% over 5000 cycles at 5 A g?1. This high performance is attributed to the enrichment of electroactive sites of GO through nitrogen moieties enhancing faradaic redox reactions and thereby the polarization at the electrode surface. 2020 Elsevier B.V. -
Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things
The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed. 2021 IEEE. -
Performance Analysis of Deep Learning Algorithms for Intrusion Detection in IoT
Due to the wide availability of IoT devices at affordable cost and the ease of use has increased IoT devices increased usage. Due to the enormous usage of the Internet of Things (IoT) devices, the security aspects related to the data are also a significant concern in this data-driven world. Negligence of security measures from users can result in severe data falsification or data thefts. In this scenario, the Intrusion Detection System has a pivotal role in IoT security. Incorporating the deep learning techniques is an effective way to predict various attacks, either known or unknown. This paper highlights the various security threats associated with IoT, the importance of deep learning in IoT intrusion detection, and various IoT intrusion detection systems using deep learning. Comparative analysis of the different deep learning techniques was performed. The results have shown Convolution Neural Networks gave high accuracy in prediction based on various evaluation metrics. 2021 IEEE. -
Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
AS-CL IDS: anomaly and signature-based CNN-LSTM intrusion detection system for Internet of Things
In recent years, the internet of things (IoT) has had a significant impact on our daily lives, offering various advantages for improving our quality of life. However, it is crucial to prioritize the security of IoT devices and the protection of user's personal data. Intrusion detection systems (IDS) play a critical role in maintaining data privacy and security. An IoT IDS continuously monitors network activity and identifies potential security risks or attacks targeting IoT devices. While traditional IDS solutions exist, intrusion detection heavily relies on artificial intelligence (AI). AI can greatly enhance the capabilities of IoT IDS through real-time monitoring, precise threat identification, and automatic response capabilities. It is essential to develop and utilize these technologies securely and responsibly to mitigate potential risks and safeguard user privacy. A hybrid IDS was proposed for anomaly-based and signature-based intrusions, leveraging convolutional neural network with long short-term memory (CNN-LSTM). The name of the proposed hybrid model is anomaly and signaturebased CNN-LSTM intrusion detection system (AS-CL IDS). The AS-CL IDS concentrated on two different IoT IDS detection strategies employing a combination of deep learning techniques. The model includes model training and testing as well as data preprocessing. The CIC-IDS 2018, IoT network intrusion dataset, MQTT-IoT-IDS2020, and BoTNeTIoTL01 datasets were used to train and test the AS-CL IDS. The overall performance of the proposed model was assessed using accepted assessment metrics. Despite reducing the number of characteristics, the model achieved 99.81% accuracy. Furthermore, a comparison was made between the proposed model and existing alternative models to demonstrate its productivity. As a result, the proposed model proves valuable for predicting IoT attacks. Looking ahead, the deployment strategy of the IoT IDS can anticipate the utilization of real-time datasets for future implementations. 2023 Jinsi Jose and Deepa V. Jose. -
Effect of digital financial literacy on digital consumer protection: Mediating role of financial self-efficacy and financial confidence
The digital consumer is gaining importance in the current digital age and there is a need to adapt to the changing context. When discussing digital consumer protection, it's critical to gauge the degree of digital financial literacy. This study explores the mediating role of financial self-efficacy and financial confidence between digital financial literacy and digital consumer protection. The study was conducted in the Indian context, and the target population was users of digital platforms for financial activities in the age group of 20 to above 60. It was found that digital financial literacy significantly impacts digital consumer protection, financial self-efficacy, and financial confidence. However, only Financial self-efficacy and digital financial literacy impact digital consumer protection. It shows that financial self-efficacy mediates the relationship between digital financial literacy and digital consumer protection. This study will benefit the users of digital platforms and assist government/ non-government agencies in designing digital consumer protection programs. 2024, IGI Global. All rights reserved. -
Digital financial literacy and its impact on financial behaviors: A systematic review
The dependence on digital financial technologies influences financial behaviors among people. This systematic review aims to identify and understand the variables interplay between digital financial literacy and financial behavior. The systematic literature review uses the PRISMA systematic review guidelines to identify and screen the literature. The results show there is a positive significant relation between digital financial literacy and financial behaviors. It was found that there are minimal studies on the variables interplaying between digital financial literacy and financial behavior. The interplaying variables identified are financial self-efficacy, financial confidence, self-control, financial autonomy, financial capability, psychological biases and digital, financial socialization, and socio-economic and demographic factors. This review develops a framework for future exploration explaining the relationship between digital financial literacy and financial behavior identifying financial wellbeing as the outcome. 2024 by IGI Global. All rights reserved. -
Fabrication of anchored complexes as electrodes for sensing heavy metal ions by electrochemical method
Anchored coordination complexes as electrochemical sensors play a significant role in the modern era. It is evident that this becomes a fact on account of their practical convenience. Furthermore, they have unlimited scope in ecological, therapeutic, experimental and biomedical applications. It has been observed that 165 such papers have reported on anchored complexes for electrochemical sensing during the past two years. While human vitality is rigorously threatened by heavy metal ions today, numerous trials are restrained for screening these in nature. This retrace highlights the electro analytical methods and the masterpiece contribution of anchored coordination complexes as electrochemical sensors for the identification of heavy metals such as indium, uranium, lead, beryllium, and mercury in 2015 and 2016. 2017, Oriental Scientific Publishing Company. All rights reserved. -
Walking an extra mile: Determinants of organizational citizenship behaviorAn exploratory study in faith-based organizations
Objective: Our study is intended to explore the factors that promote organizational citizenship behaviors (OCB) in faith-based organizations, which has the potential to extend the scholarly conversation around a previously unexplored context. Methods: Our study used a purposive, homogeneous sampling technique in selecting the participants. We interviewed 30 employees who have at least 5 years of experience in faith-based organizations. We conducted detailed interviews and subsequent analysis involved a rigorous six-step thematic analysis process to better understand the phenomena being studied. Results: Our study revealed the significance of OCB in faith-based organizations and further elucidated those factors that determine the display of OCBs. Our findings have the power to enhance the existing comprehension of OCBs in different environments, specifically those present in faith-based organizations. Our study broadens our conceptual understanding of OCB in faith-based organizations by adding one more dimension to the existing framework of P. MPodsakoff etal. (1990). Conclusion: Our findings have important implications for organizational scholars in faith-based organizations. Our new conceptual framework offers insights into the distinct characteristics of OCBs in faith-based organizations and suggests directions for future scholars to engage with OCBs from different contexts. 2024 by the Southwestern Social Science Association. -
Agricultural waste valorisation Novel Areca catechu L. residue blended with PVA-Chitosan for removal of chromium (VI) from water Characterization, kinetics, and isotherm studies
Arecanut, an industrial crop prevalent in tropical regions such as India, Sri Lanka, and parts of Southeast Asia, generates significant agricultural waste during processing. This study explores a waste-to-wealth approach by incorporating arecanut organic residue into Polyvinyl alcohol (PVA) - Chitosan blends via an eco-friendly continuous stirring method to develop an adsorbent film for removing chromium (VI) from water. Morphological analyses confirmed enhanced surface area, porosity, and roughness in the blended films. XRD and FTIR analyses indicated a semi-crystalline nature with a decrease in the characteristic peak intensity of PVA and chitosan, confirming the incorporation of arecanut residue. Optimal conditions identified OR-4 film, using 0.4 g of adsorbent, achieving 88.68 % removal of 173 mg/L chromium (VI) at pH 9.0, within 45 minutes at 40C. SEM images demonstrated significant surface roughness reduction before and after adsorption, confirming chromium adsorption. Kinetic studies revealed a pseudo-second-order model and adsorption isotherms confirmed film surface heterogeneity. This research advances eco-friendly materials for water purification and offers a sustainable solution for managing agricultural residues. 2024 Elsevier B.V. -
Optimizing energy consumption in wireless sensor networks using python libraries
Wireless sensor networks (WSNs) are widely utilized in various fields, including environmental monitoring, healthcare, and industrial automation. Optimizing energy consumption is one of the most challenging aspects of WSNs due to the limited capacity of the batteries that power the sensors. This chapter explores using Python libraries to optimize the energy consumption of WSNs. In WSNs, various nodes, including sensor, relay, and sink nodes, are introduced. How Python libraries such as NumPy, Pandas, Scikit-Learn, and Matplotlib can be used to optimize energy consumption is discussed. Techniques for optimizing energy consumption, such as data aggregation, duty cycling, and power management, are also presented. By employing these techniques and Python libraries, the energy consumption of WSNs can be drastically decreased, thereby extending battery life and boosting performance. 2023, IGI Global. All rights reserved. -
Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends
The stock market is an essential component of the world economy and significantly impacts how different countries handle their finances. Predicting stock prices has gained popularity recently since it can offer traders, investors, and policymakers useful information. Making informed financial decisions, lowering risk, and maximizing returns can all be facilitated by accurate stock price projections. Stock price prediction is a current research subject due to improvements in machine learning (ML) techniques, and several methodologies have been put forth in the literature. To increase the accuracy of stock price prediction, one method combines the feature extraction ability of convolutional neural networks (CNNs) with the classification strength of support vector machines (SVMs). CNNs are a subclass of neural networks that have excelled in voice and picture recognition. They can be taught to extract valuable features from the supplied data automatically. Contrarily, SVMs are a well-liked machine learning (ML) technique that has been applied for regression and classification tasks. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
A novel model for speech to text conversion /
International Refered Journal of Engineering And Science, Vol-3 (1), pp. 39-41,ISSN-2319-183X. -
Energy efficient routing protocols for wireless sensor networks
Wireless Sensor Networks (WSNs) have gained universal attention now a day???s owing to the advancements made in the fields of information and communication technologies and the electronics field. This innovative sensing technology incorporate an immense number of sensor nodes or motes set up in an area to perceive any continuously fluctuating physical phenomena. These tiny sensor nodes sense and process the sensed data and transfer this information to a base station or sink via radio frequency (RF) channel. The small size of these sensors is an advantage as it can be easily embedded within any device or in any environment. This feature has attracted the use of WSNs in immense applications especially in monitoring and tracking; the most prominent being the surveillance applications. But this tiny size of sensor nodes restricts the resource capabilities. Usually the WSNs are installed in application areas where the human intervention is quite risky or difficult. The sensed information might be needed to take critical decisions in emergency applications. So maintaining the connectivity of the network is of utmost importance. The efficient use of the available resources to the maximum extend is a necessity to prolong the network lifetime. If any node runs out of power, the entire network connectivity collapses and intend of the deployment might become futile. Because of this reason most of the research in the area of WSNs has concentrated on energy efficiency where the design of energy efficient routing protocols plays a major role. This research work titled ???Energy Efficient Routing Protocols for Wireless Sensor Networks??? proposes to develop energy efficient routing protocol strategies so as to enhance the lifetime of the WSNs. A thorough study of the existing literature serves as the back bone for attaining acquaintance concerning the pertinent scenario, the problems faced and the application of the WSNs. The use of clustering and sink mobility to enhance the energy utilisation is explored in this research. A modification of the most traditional energy efficient routing protocol for WSNs, LEACH (Low Energy Adaptive Clustering Hierarchy) is implemented initially by modifying the clustering mechanism. An enhancement of it by incorporating sink mobility, to further augment the energy efficiency is executed next. A modification of HEED (Hybrid Energy Efficient Distributed Clustering Hierarchy) protocol using the unequal clustering technique is also proposed. The modified protocols are simulated using MATLAB under different circumstances by varying the number of sensor nodes and the area of deployment. These modified protocols are intended for delay tolerant applications that require periodic sensing. The performance of the modified protocols is evaluated using metrics like residual energy of the network, packet delivery ratio, energy consumed by the network, delay, and the number of live nodes. The simulation outcomes showcased the effectiveness of the modified protocols compared to the relevant existing protocols in literature. -
Energy efficient routing protocols for wireless sensor networks
Wireless Sensor Networks (WSNs) have gained universal attention now a day s owing to the advancements made in the fields of information and communication technologies and the electronics field. This innovative sensing technology incorporate an immense number of sensor nodes or motes set up in newlinean area to perceive any continuously fluctuating physical phenomena. These newlinetiny sensor nodes sense and process the sensed data and transfer this information to a base station or sink via radio frequency (RF) channel. newlineThe small size of these sensors is an advantage as it can be easily embedded within any device or in any environment. This feature has attracted the use of WSNs in immense applications especially in monitoring and tracking; the most prominent being the surveillance applications. But this tiny size of sensor nodes restricts the resource capabilities. Usually the WSNs are installed in application areas where the human intervention is quite risky or difficult. The sensed information might be needed to take critical decisions in emergency applications. So maintaining the connectivity of the network is of utmost newlineimportance. The efficient use of the available resources to the maximum extend newlineis a necessity to prolong the network lifetime. If any node runs out of power, the newlineentire network connectivity collapses and intend of the deployment might become futile. Because of this reason most of the research in the area of WSNs has concentrated on energy efficiency where the design of energy efficient routing protocols plays a major role. newlineThis research work titled Energy Efficient Routing Protocols for Wireless Sensor Networks proposes to develop energy efficient routing protocol strategies so as to enhance the lifetime of the WSNs. A thorough study of the existing literature serves as the back bone for attaining acquaintance concerning the pertinent scenario, the problems faced and the application of the WSNs. newlineThe use of clustering and sink mobility to enhance the energy utilisation is explored in this research.