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Fabrication of electrochemical sensors for pharmaceuticals and biologically significant molecules
Newer properties of electrochemical sensors for various target molecules are being developed in continuum. Such sensors have attracted a lot of attention due to their simplicity, high sensitivity and trace-level detection of analytes in real samples. Sensor is a system that on stimulation by any form of energy undergoes change in its own state which helps to analyze the stimulant qualitatively and quantitatively. In the thesis studies presented, we have also described the development of electrochemical sensors for the determination of pharmaceuticals and biologically significant molecules. This can be achieved by modifying newlineelectrodes by electrochemical method. Electrode modifiers like metal nanoparticles dispersed on conducting polymers and carbon nanospheres were employed for modification of carbon fiber paper working electrode substrate. These modified electrodes were physicochemically characterized by X-ray diffraction (XRD), Field emission scanning newlineelectron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), Transmission electron microscopy (TEM), Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron newlinespectroscopy (XPS) and electrochemically characterized using Cyclic voltammetry and Electrochemical impedance spectroscopy (EIS). newlineThe modified electrodes have exhibited remarkable electrocatalytic behaviour towards oxidation or reduction of chosen analytes. These modified electrodes were used as electrochemical sensors after optimization of experimental conditions. Under optimal conditions, the sensors have displayed significantly an ultra-low level detection limit with wide linear response and high selectivity towards analyte in the newlinepresence of other interfering substances. newlineThe ultrasensitivity and reliability of the fabricated sensors towards analyte of interest were effectively determined in real samples. -
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LiST: A Lightweight Framework for Continuous Indian Sign Language Translation
Sign language is a natural, structured, and complete form of communication to exchange information. Non-verbal communicators, also referred to as hearing impaired and hard of hearing (HI&HH), consider sign language an elemental mode of communication to convey information. As this language is less familiar among a large percentage of the human population, an automatic sign language translator that can act as an interpreter and remove the language barrier is mandatory. The advent of deep learning has resulted in the availability of several sign language translation (SLT) models. However, SLT models are complex, resulting in increased latency in language translation. Furthermore, SLT models consider only hand gestures for further processing, which might lead to the misinterpretation of ambiguous sign language words. In this paper, we propose a lightweight SLT framework, LiST (Lightweight Sign language Translation), that simultaneously considers multiple modalities, such as hand gestures, facial expressions, and hand orientation, from an Indian sign video. The Inception V3 architecture handles the features associated with different signer modalities, resulting in the generation of a feature map, which is processed by a two-layered (long short-term memory) (LSTM) architecture. This sequence helps in sentence-by-sentence recognition and in the translation of sign language into text and audio. The model was tested with continuous Indian Sign Language (ISL) sentences taken from the INCLUDE dataset. The experimental results show that the LiST framework achieved a high translation accuracy of 91.2% and a prediction accuracy of 95.9% while maintaining a low word-level translation error compared to other existing models. 2023 by the authors. -
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Enhancing IoT Security Through Deep Learning-Based Intrusion Detection
The Internet of Things (IoT) has revolutionized the way we interact with technology by connecting everyday devices to the internet. However, this increased connectivity also poses new security challenges, as IoT devices are often vulnerable to intrusion and malicious attacks. In this paper, we propose a deep learning-based intrusion detection system for enhancing IoT security. The proposed work has been experimented on IoT-23 dataset taken from Zenodo. The proposed work has been tested with 10 machine learning classifiers and two deep learning models without feature selection and with feature selection. From the results it can be inferred that the proposed work performs well with feature selection and in deep learning model named as Gated Recurrent Units (GRU) and the GRU is tested with various optimizers namely Follow-the-Regularized-Leader (Ftrl), Adaptive Delta (Adadelta), Adaptive Gradient Algorithm (Adagrad), Root Mean Squared Propagation (RmsProp), Stochastic Gradient Descent (SGD), Nesterov-Accelerated Adaptive Moment Estimation (Nadam), Adaptive Moment Estimation (Adam). Each evaluation is done with the consideration of highest performance metric with low running time. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection
The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparative analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model. (2023), (Science and Information Organization). All Rights Reserved. -
Subjectivity Analysis Using Social Opinion on Stress and Strain During Covid-19 Pandemic
The psychological health of several people across the globe has been under great risk newlineas a result of the COVID-19 pandemic that shook the entire world. The ubiquitous newlinepandemic had created a tectonic shift in everyone s life. The lives of people have newlineundergone a severe transition with strict measures like lockdown and social distancing newlineimposed by governments of several countries to stop the spread of the viral infections. newlineCoping through the adverse situation has been quite onerous causing stress among the people. The transition from normal life to a life filled with several restrictions has newlinebeen stressful and strenuous. A state of emotionally or physically being tensed can be newlineconsidered as stress. Stress can cause frustration, depression, nervousness and other mental health issues. Stress also leads to strain. Social media networking sites like newlineX(Earlier Twitter) and Facebook have emerged to become popular. During the times of lockdown and social distancing the social media networking sites have been a great newlineplatform for expressing opinions, exchange of ideas and thoughts. People have expressed their stressful situations and coping mechanisms through tweets , Facebook newlineposts and several other social media sites during the pandemic. The underlying stress newlineand strain of a person can be analyzed through the posts shared by the person through the social media sites. Early detection of the prevalence of the stress and strain is important, as medical help can be sought quickly and the person affected can be back to normalcy. Subjectivity analysis is the study that deals with analyzing the emotions, feelings, attitudes and polarity of opinions considering any subject matter. newlineThe present research focuses on subjectivity analysis through social opinion mining newlineduring the COVID-19 pandemic. Social opinion mining incorporates Natural Language Processing and Computational Linguistics that identifies the subjectivity across the posts of social media. -
Subjectivity analysis using social opinion mining on stress and strain during covid 19 pandemic
The psychological health of several people across the globe has been under great risk newlineas a result of the COVID-19 pandemic that shook the entire world. The ubiquitous newlinepandemic had created a tectonic shift in everyone s life. The lives of people have newlineundergone a severe transition with strict measures like lockdown and social distancing newlineimposed by governments of several countries to stop the spread of the viral infections. newlineCoping through the adverse situation has been quite onerous causing stress among the people. The transition from normal life to a life filled with several restrictions has newlinebeen stressful and strenuous. A state of emotionally or physically being tensed can be newlineconsidered as stress. Stress can cause frustration, depression, nervousness and other mental health issues. Stress also leads to strain. Social media networking sites like newlineX(Earlier Twitter) and Facebook have emerged to become popular. During the times of lockdown and social distancing the social media networking sites have been a great newlineplatform for expressing opinions, exchange of ideas and thoughts. People have expressed their stressful situations and coping mechanisms through tweets , Facebook newlineposts and several other social media sites during the pandemic. The underlying stress newlineand strain of a person can be analyzed through the posts shared by the person through the social media sites. Early detection of the prevalence of the stress and strain is important, as medical help can be sought quickly and the person affected can be back to normalcy. Subjectivity analysis is the study that deals with analyzing the emotions, feelings, attitudes and polarity of opinions considering any subject matter. newlineThe present research focuses on subjectivity analysis through social opinion mining newlineduring the COVID-19 pandemic. Social opinion mining incorporates Natural Language Processing and Computational Linguistics that identifies the subjectivity across the posts of social media.
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Sentiment Analysis On Covid-19 Related Social Distancing Across The Globe Using Twitter Data
Covid 19 pandemic has devastated the lives of several people across the globe. Social distancing is considered a major preventive measure to stop the spread of Covid 19. The practice of social distancing has caused a sense of loneliness and mental health problems in society. The aim of this study is to consider global tweet data with social distancing keywords for analyzing the sentiments behind them. Classification of tweets as positive or negative is carried out using Support Vector Machine and Logistic Regression. The Electrochemical Society -
Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets
Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Novel Deep Neural Network Based Stress Detection System
Stress is a state of tension on an emotional or bodily level. Frustration, despair, anxiety, and other mental health problems can all be brought on by Stress. Strain is a side effect of Stress. People can openly share their views and opinions on social media networking sites like Twitter and Facebook, which are highly popular. The COVID 19 pandemic has wreaked havoc on millions of peoples lives all across the world. The public has experienced Stress as a result of the various measures employed to stop the spread of COVID 19, including confinement and social isolation. The current research seeks to develop an unique COVID 19 scenario-based deep neural network-based Stress detection system using tweets related to COVID 19. We use deep learning to create three models. RNN with single LSTM layer, two layers of LSTM with RNN followed by bidirectional LSTM layer is built to detect Stress for the considered dataset. A number of recurrent neural networks are built upon the Keras layers. The optimization algorithm called RMSProp and Sigmoid activation function is used. It is observed that RNN with 2 layers of LSTM outperforms the other deep learning architectures constructed. 2023 American Institute of Physics Inc.. All rights reserved. -
A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data
The advances in digitalization have resulted in social media sites like Twitter and Facebook becoming very popular. People are able to express their opinions on any subject matter freely across the social media networking sites. Sentiment analysis, also termed emotion artificial intelligence or opinion mining, can be considered a technique for analyzing the mood of the general public on any subject matter. Twitter sentiment analysis can be carried out by considering tweets on any subject matter. The objective of this research is to implement a novel algorithm to classify the tweets as positive or negative, based on machine learning, deep learning, the nature inspired algorithm and artificial neural networks. The proposed novel algorithm is an ensemble of the decision tree algorithm, gradient boosting, Logistic Regression and a genetic algorithm based on the auto-encoder technique. The dataset under consideration is tweets on COVID-19 in May 2021. 2024 Taylor & Francis Group, LLC. -
Design of a square-shaped broadband antenna with ground slots for bandwidth improvement
This paper portrays the design of a compact square-shaped microstrip broadband antenna using ground slots. Polygon shaped slots are placed on the ground under the feed line for bandwidth improvement. Similarly, rectangular slots are placed on the square patch for gain enhancement. Effect of these slots on the performance of the antenna in terms of impedance bandwidth, gain and directivity are studied. Results of simulation tests show that a ground slot with proper dimensions placed under the feed line can improve the impedance matching and hence increase the bandwidth without affecting much the performance of the antenna. This compact antenna of size 9.098 x 9.098 mm can be very useful for applications where size is a major constraint. Simple microstrip feed is used to feed the patch. The percentage bandwidth of this antenna is 75.57 %. 2018 Authors. -
Divergent Synthesis of Azole Tailored Compounds and Their Biological and Photoluminescence Applications
Producing a library of diverse compounds with minor structural differences can provide newlinevaluable information related to the structure-activity relationship (SAR), which would not be possible by studying just one molecule. The main goal of the divergent synthesis approach is to efficiently create a collection of valuable compounds, which is different from the traditional methods of making compounds in a linear or convergent way. This approach, known as divergent synthesis, helps select the best compound from the group for its applications. In the newlinecurrent study, the focus is on synthesizing different types of azoles, such as Thiazole Schiff bases, fused tetrazoles, substituted imidazole, and 1H-tetrazoles, and exploring their potential uses in biological and photoluminescence studies. Several methods were utilized to synthesize the derivatives of azole compounds. The synthesized molecules were examined and identified using techniques like 1HNMR, 13CNMR, Mass spectrometry, and IR spectroscopy. After creating a library of molecules, they were evaluated for their potential applications in biology and photoluminescence. The most promising molecule was selected from the preliminary evaluation for further investigation. newlineThiazole Schiff bases were synthesized, and their photoluminescence properties were newlineinvestigated. Among the synthesized compounds, the bromo derivative showed the most promising results in developing fluorescent organic nanoparticles with versatile applications. The compound delivered exceptional results in aggregation-induced emission (AIE), viscochromism, detection of Al3+ions, pH sensing, latent fingerprint detection, and cell imaging. Synthesis of fused azole-derivatives was accomplished using the organo-catalyst 10- newlinecamphor sulfonic acid. Detailed optimization and mechanistic studies were conducted, along newlinewith evaluating the antifungal activity against Candida tropicalis ATCC 10231 for the newlinesynthesized compounds. -
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Organizational Sustainability:A Study of Corporate Organizations in the Indian Context
Creating and Sustaining an Organization is an all time challenge. The primary research question is mainly of an explorative nature, seeking to comprehend how the Indian companies view and act upon sustainability. The study focused on the Corporate Organization, meaning Multi National Corporations, Public Sector Undertakings and other Private Organizations. The findings of the study facilitate recommendations to the various organizations to improve the managerial practice and guide them to the ways of sustainability. The aim of the study is to examine the different stages of development of various organizations that best describes the organization and strategy of the organization in sustaining the organization. This study is guided to analyze and understand the capacity of the organizations to respond to changing environments (Sustainability). The scope of sustainability are, the Environment and the Social dimension, Institutional / organizational dimension, Profit making / Economic dimension. Sustainability is a contestable concept that can be examined from the dimensions mentioned above. Organizational Sustainability is often guided by vision, mission, policy, planning, financial situation , human resource management, marketing activities, business ethics, organizational culture, organizational climate, business practices, employee treatment, community engagement ( social responsibility practices) etc. The design of the study is based on the Management and Organizational Sustainability Tool (MOST). The first objective of the study is, to investigate if there is a relationship between the vision and mission with strategy, structure and systems in the organizations. newlineIndia, a land of rich culture and heritage, has to an extent made it possible for its firms to have a culture passed to the employees and have them engaged in the organizational sustainability practices, and being socially responsible. The culture of an organization is intertwined with the philosophy, purposes, functions and structures. -
Impact of Lysinibacillus macroides, a potential plant growth promoting rhizobacteria on growth, yield and nutritional value of tomato plant (Solanum lycopersicum L. F1 hybrid Sachriya)
Plant growth promoting bacteria enhance the growth in plants by solubilizing insoluble minerals, producing phytohormones and by secreting enzymes that resist pathogen attack. The present study was aimed at identifying the potential of Lysinibacillus macroides isolated from pea plant possessing rich microbial rhizobiome diversity in promoting the growth of tomato plant (Solanum lycopersicum L.). Potential of L. macroides in the promotion of S. lycopersicum L. growth by increased shoot length, terminal leaf length and breadth was assessed. Anatomical sectioning of stem and root revealed no varied cellular pattern indicating that the supplemented bioculture is not toxic to S. lycopersicum. Plantlets treated with L. macroides along with organic compost showed an increased total phenol content (17.580.4 mg/gm) compared to control samples (12.440.41 mg/g). Carbohydrate content was noticed to be around 1.3 folds higher in the L. macroides plus compost mixture supplemented slots compared to control sample. Significant increase in shoot length was evident in the L. macroides plus compost supplied slots (23.42.7 cm). Plant growth promoting properties might be due to the nitrogen fixing activity of the bacteria which enrich the soil composition along with the nutrients supplied by the organic compost. Rich microbial rhizobiome diversity in pea plant and the usage of L. macroides from a non-conventional source improves the diversity of the available PGPR for agricultural practices. Further research is needed to detect the mechanism of growth promotion and to explore the plant microbe interaction pathway. Jyolsna et al. (2021). -
Pollution forecast of united states using holt-winter exponential method
The United States is the world's most developed country and one of the top ten most air polluted countries in the world. Though the population is not very dense as in India or China, people face immense health problems. The US government is taking a lot of initiatives than any other government globally. However, it still faces issues. This paper mainly focuses on developing a forecasting model of the top four pollutants like SO2, NO2, CO, O3 that will help the country take necessary actions for the near future. This paper involves the secondary data of the daily pollution collected and merged for all states from 2007 to 2017. The forecast will throw the better output at the pollutants for the next four years, until 2021. The findings revealed that despite the increased GDP, the country had controlled the pollution level. NO2 has decreased to a better level. O3 and CO2 are also decreasing but has slight fluctuations. It will take some time to stabilize. SO2 had an increased level till 2017 and has started reducing afterwards. 2021 Ecological Society of India. All rights reserved. -
ANN Based MPPT Using Boost Converter for Solar Water Pumping Using DC Motor
The solar DC pump system is simple to set up and run completely on its own without the need for human intervention. Solar DC pumps require fewer solar panels to operate than AC pumps. Solar PV Arrays, a solar DC regulator, and a DC pump make up the Solar DC Pump system. The nonlinear I-V characteristics of solar cells, PV modules have average efficiency compare to other forms of energy, and output power is affected by solar isolation and ambient temperature. The prominent factor to remember is that there will be a significant power loss owing to a failure to correspond between the source and the load. In order to get the most power to load from the PV panel, MPPT is implemented in the converter circuit using PWM and a microcontroller. In order to give the most power to load from the source, the solar power system should be designed to its full potential. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Synthesis, Characterization and Studies of Hydrazine Based Polyfunctional Ligands and their Metal Chelates
Eight new hydrazine-based zinc (II), copper (II) complexes were synthesized by reacting Zn (OAc)2.2H2O and Cu(CH3COO)2 with N'??(3,5-dibromo-2-hydroxy benzylidene) benzohydrazide (H2L1) and N'??(3,5-dibromo-2-hydroxy benzylidene) nicotinichydrazide (H2L2) respectively. The synthesized complexes were characterized by CHN analyses, IR, UV and 1H NMR. Based on these studies, square planar and octahedral geometries of the metal complexes were revealed. The synthesized metal complexes named [Zn(H2L1)2](OAc)2, [Zn(H2L1)Py](OAc)2, [ZnL2]2, [ZnL2Py], [CuL1]2, [CuL1Py], [CuL2]2 and [CuL2Py]. The formed metal complexes were investigated for DNA binding studies by fluorescence and UV spectroscopy using calf thymus DNA (CT-DNA) and DNA cleavage studies against pBR322 DNA. Both the ligands and their corresponding metal complexes showed the ability for binding to DNA through intercalation/ electrostatic binding.