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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. -
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. -
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. -
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. -
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 -
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. -
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. -
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. -
A Study on Indian Foriegn Exchange Market Efficiency - Application of Random Walk Hypothesis
International Journal of Research in Computer Application & Management Vol. 2, Issue 10, pp. 138-142, ISSN No. 2231-1009 -
The nexus between demogaphics and investment behaviour /
Asian Journal Management, Vol.8, Issue 2, pp.361-369, ISSN: 2321-5763 (Online) 0976-495X (Print). -
Financial behavior of IT professionals: A case study of Bengaluru city /
Al-Barkaat Journal of Finance & Management, Vol.8, Issue 2, pp.19-31, ISSN: 0974-7281 (Print), 2229-4503 (Online). -
Saving and investment behaviour of information technology professionals - An empirical analysis /
Asian Journal Of Research In Business Economics And Management, Vol.7, Issue 6, pp.71-91, ISSN: 2249-7307. -
An empirical analysis of price discovery in spot and futures market of gold in India /
Pacific Business Review International, Vol.7, Issue 10, pp.80-88 -
Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments
Air pollution poses a significant environmental and health challenge in Delhi, India. This research focuses on predicting the Air Quality Index (AQI) for Delhi utilizing machine learning techniques. The research methodology encompasses comprehensive steps such as data collection, preprocessing, analysis, and modeling. Data comprising various pollutants and meteorological parameters were gathered from the Central Pollution Control Board (CPCB) spanning from January 1, 2016, to December 30, 2022. Missing values were imputed using the IterativeImputer method with RandomForestRegressor as the estimator. Data normalization and variance reduction were achieved through Box-Cox transformation. Spearman Rank Correlation analysis was employed to explore relationships between features and AQI. Initial evaluation of nine machine learning algorithms identified Random Forest and XGBoost as the top performers based on accuracy. These algorithms were further optimized using 5-fold cross-validation with RandomizedSearchCV. The results demonstrated the efficacy of both algorithms in AQI prediction. Notably, PM2.5 and CO concentrations emerged are most influential features, highlighting the potential for AQI improvement in Delhi through the reduction of these pollutants. This research distinguishes itself through a meticulous examination of the complex interconnections between pollutants and AQI, providing invaluable insights to inform targeted interventions and enduring policies geared towards improving air quality in Delhi. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
pH-indicator based on delignified jute fiber and red cabbage anthocyanins for monitoring fish spoilage using a smartphone application
Halochromic materials that show visible color changes in response to changes in pH are suitable for the real-time monitoring of fish spoilage. In this study, an easy-to-use, simple, inexpensive, and non-toxic fish freshness indicator was fabricated by combining delignified jute (Corchorus olitorius) fibers and anthocyanins (halochromic materials) from red cabbage (DFA: Delignified jute fibers incorporated with anthocyanins). A single-step decolorization/delignification using solar irradiation along with NaOH and H2O2 treatment was used for modifying the jute fibers. This method helps to overcome the self-color, mitigates the lack of affinity of jute fibers towards anthocyanins and preserves the lignin so that the strength of the fiber is not impacted. A smartphone-based color analysis was used for real-time fish quality monitoring using DFA. To the best of our knowledge, there are no reports on the use of jute fibers as substrates to incorporate anthocyanins for food spoilage monitoring. The indicator displayed an observable color response to the pH and varying concentrations of amine compounds. During the storage of fish (mackerel), the colorimetric indicator showed a visible color change from pink (for fresh fish) to blue (for spoiling fish) and then to green (for spoiled fish), corresponding to changes in pH and total volatile basic nitrogen. To offer a straightforward quantitative assessment of color changes, we utilized the freely available Android application Color Grab to measure the color using RGB and L*, a*, and b* indices. The DFA indicator providing naked-eye analysis has the potential to be an effective tool for real-time monitoring of on-site food spoilage by non-specialized personnel in resource-limited areas. 2024 Elsevier B.V. -
Multifunctional electrospun membranes incorporated with metal oxide nanoparticles, cellulose acetate, and polyvinylpyrrolidone for wastewater treatment: Oil/water separation, dye adsorption, and dye degradation
Multifunctional membranes have gained considerable attention as useful materials for the treatment of complex wastewater that contains dye and oil substances. Electrospun nanofiber membranes (ENM) have substantial advantages and potential for complex wastewater remediation, owing to their unique properties. In this study, an environmentally compatible ENM is fabricated by incorporating photocatalytic metal oxide nanoparticles of zinc oxide (ZnO) or silver-zinc Oxide (Ag-ZnO) into cellulose acetate (CA)/polyvinylpyrrolidone (PVP) nanofibers using electrospinning. Composite membranes ZnO/CA/PVP, Ag-ZnO/CA/PVP, ZnO/DCA/PVP (DCA: deacetylated cellulose acetate), and Ag-ZnO/DCA/PVP (deacetylated) were employed for oilwater emulsion separation, owing to their superhydrophilic and underwater superoleophobic nature, photocatalytic dye degradation due to the presence of ZnO or Ag-ZnO, and dye adsorption resulting from their high surface area. The composite membranes showed more than 95% efficiency for oil/water separation, malachite green adsorption, and photocatalytic methylene blue degradation. These membranes displayed simultaneous oilwater and dye separation efficiency, as well as antibacterial properties. The membrane we present here provides a simple and effective platform for wastewater remediation with a low energy consumption. 2024 Elsevier B.V. -
Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques
Sentiment analysis plays a vital role in real time environment for knowing the history of a product or any other specific entity. Due to large number of users in the www, chances are there that many fake users may upload the fake reviews to damage the business for the sake of money. Identifying the fake reviews or percentage of fake content in the review is yet a challenging task. In this paper, an attempt has been made to find the percentage of fake in the review data. Two methodologies are combined to address this issue. Concept of spelling checking, topic modelling and deep learning for context extraction is extensively used to build the effective model. Proposed technique is exhaustively checked for efficiency with many trails of experiments. Also, the training and testing samples were shuffled for experimentation. The results of the models show its goodness. The details of the results can be found at experiments section. 2024 The Author(s) -
Analysis of value and growth styles of investing : A study on nifty 100 index stocks of NSE /
Asian Journal Of Research In Business Economics And Management, Vol.7, Issue 5, pp.165-177, ISSN: 2249-7307. -
Dynamics of Indian stock market integration with global stock markets /
Asian Journal Of Management, Vol.8, Issue 3, pp.559-568, ISSN: 2321-5763 (Online) 0976-9495X (Print). -
Statistical features from frame aggregation and differences for human gait recognition
Human gait recognition, an alternate biometric technique, received significant attention in the last decade. As many gait recognition applications require real-time response, the primary concern is to design efficient and straightforward gait features for human recognition. In this work, two novel gait features are proposed. Both features are designed by exploring the dynamic variations of different body parts during a gait cycle. The first feature set is based on one-against-all gait frame differences for person identification. This novel approach divides each frame in a gait cycle to blocks, compute the block sum, and then find the difference of respective block sum between the first frame and the rest. The second feature set is defined on the first-order statistics of the normalized sum of the frames in a cycle. Two other existing features- Centroid of Silhouette frames and feature values defined on Change Energy Images are also considered. Feature level fusion is realized by considering the different combinations of the four types of features. Experiments carried out with the CASIA Gait Dataset B demonstrated the proposals merit with high recognition accuracy. The outcome of the investigations is promising when compared to recent contributions. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.