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A Slotted Circular Patch Antenna with Defected Ground for Sub 6 GHz 5G Communications
In this paper, a slotted circular patch antenna with Defected Ground Structure (DGS) is presented. The slots created on radiating element and the defect introduced on the ground plane shifted the resonance frequency from 2.49 GHz to 1.17 GHz. This corresponds to 53% reduction in size at 1.17 GHz. The proposed antenna is designed on FR-4 substrate (r=4.4) with thickness of 1.6 mm. Simulations are carried out using HFSS Ver. 18.2. The simulated reflection coefficient of Circular Patch Antenna (CPA) at 2.49 GHz, Slotted Circular Patch antenna (SCPA) at 2.34 GHz and Slotted Circular Patch antenna with Defected Ground Structure (SCPA-DGS) at 1.17 GHz are - 28.7 dB, -31.33 dB and -11.03 dB respectively. For validating the simulated design, SCPA-DGS is fabricated and measured its reflection coefficient and VSWR using Vector Network Analyzer (Anritrsu S820E). The measured and simulated values are very well matched with each other. Therefore the proposed antennas may be used in sub 6 GHz 5G communication applications. 2022 IEEE. -
A Smart Academic Ecosystem Framework for Enhancing Digital Skills and Startup Success Among Potential Women Entrepreneurs
Lack of digital literacy is still a significant impediment for women entrepreneurs to participate and succeed in sectors that rely on technology infrastructure, as mentoring and incubation support is uneven, even for those who are skilled. Conventional academic programs tend to lack well-defined links between structured digital education and entrepreneurial careers, and, as a consequence, gaps between learning outcomes and startup success result. This paper introduces a Smart Academic Ecosystem (SAE) Framework, with tailored and interactive digital adaptation, AI-based mentorship, and dedicated startup incubation, pleading for a standard, one-size-fits-all system. The framework is implemented with a layered architecture comprising data ingestion (from learning management systems), feature stores (learner profiling), and algorithmic modules (knowledge tracing, contextual learning path recommendations, graph-based mentor matching, and venture readiness scoring). Fairness-enabling interventions and privacy-preserving analytics are built into the system to support fairness and trust. A pilot evaluation with early-stage women entrepreneurs identified substantial gains: digital skills mastery increased by more than 20%, startup initiation improved by 12 percentage points, and equity gaps in digital confidence and access were significantly narrowed. Findings emphasize that the SAE model leads not only to faster development of digital capability but also to higher chances of entrepreneurial success. This paper provides a replicable, standards-based, and computer-science-centred framework for academic institutions to encourage women entrepreneurs and innovation ecosystems. 2025 IEEE. -
A Smart Internet of Things (IoT) Enabled Agricultural Farming System
Industry 4.0 has brought about a profound revolution in recent times. This advancement profoundly impacted technology usage in every aspect and has significantly improved businesses. Agriculture is one of the evergreen economic contributors to Indias GDP. With improvements in adaptability in this sector, the time is ripe for instituting IoT (Internet of Things)-based smart agriculture. Water scarcity and drastic climate change are real issues affecting crop yields, leading to the failure in the timely fulfillment of market demand (Nawandar 2019). The authors have collaborated to address these concerns by creating a system comprising a functional hardware prototype and an android application for regulating irrigation and temperature. The introduction of IoT (Internet of Things) automates crop monitoring and reduces labor costs. By using IoT, (Internet of Things) an earmarked agricultural field is covered with sensors. The sensors are concealed so as not to be affected by the bleakness of the external environment. These sensors work in tandem with drip irrigation following the sensed climatic conditions. The water is pumped directly to the root zone in an optimally sensed manner. The authors developed and tested the system successfully in a greenhouse system. The process initially aims to extract the values of soil parameters by using IoT (Internet of Things) sensors and appropriately control the watering of crops, thus enabling the cultivation of crops even in a hot and dry climate. Crops can be irrigated from a remote location and their temperature can be meticulously regulated to ensure they remain within an optimal range. Water utilization for agricultural crops is optimized with the use of automated irrigation systems that use W.S.N (Wireless - Sensor-Networks) and G.P.R.S (General-Packet-Radio-Service) modules. The algorithm employed in the system to control water usage is based on the needs of the crop and the terrain. The entire system is powered by photovoltaic panels, which are useful in rural and isolated areas without electricity (Raut and Shere 2014). A cellular network is used for duplex communication. Continuous monitoring and irrigation schedule programming are used by web apps to manage irrigation. This is also possible using a browser and web pages. A system with three identical automatic irrigation systems can save water use by up to 90%. 2024 by Nova Science Publishers, Inc. All rights reserved. -
A Smartphone Based Robust Model for Intelligent Prediction of Thyroid Disease
The thyroid is an integral organ that exerts vital functionality in regulating the bodys functionality in many different ways. Intelligent thyroid disease prediction is very crucial. Through this paper we will describe a smartphone model for predicting whether a patient has thyroid disease based on certain attributes whose values are given by the user. Here, a profound machine learning algorithm named Random Forest will help us predict thyroid disease. By exercising Random Forest within a mobile application, the users can learn about any potential thyroid disease disorder and thus seek medical help in time. This will help improve both the patients and the medical providers quality of care and the help received. With the integration of the Random Forest algorithm along with the mobile application it will prove to be a vital tool which will help everyone in the healthcare fraternity because of its availability and accessibility. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Smartphone Coupled Freshness Indicator Prepared by Rub-coating of Hibiscus Flowers on Paper substrates for Visual Monitoring of the Spoilage of Milk
This study developed an inexpensive and easy-to-use milk freshness indicator (H-Paper) by rub-coating hibiscus flowers onto paper. As per the best of our knowledge, this is the first example of a food quality indicator prepared by direct hand-rubbing of a natural halochromic material onto paper substrates. Unlike the previously reported colorimetric indicators from natural sources, our indicator does not require the extraction of anthocyanins from the natural product. The H-Paper undergoes a quantifiable color change from green to purple to pink corresponding to fresh, spoiling, and spoiled milk samples. Digital photographs of H-Paper treated with milk samples were acquired using a smartphone, and RGB indices were analyzed using a free application. The red chromatic shift was used as a parameter to quantify color change of H-Paper to follow the spoilage of milk. Anthocyanins present in the hibiscus flowers are responsible for this colorimetric response. This work demonstrates a proof-of-concept for directly rub-coating halochromic materials onto a substrate to fabricate food freshness indicators. The H-Paper indicator has advantages of naked-eye analysis, consumer-friendliness, and instrument-free operation. This requires neither any laboratory accessories nor the expertise of a trained analyst for its preparation and operation, which is appropriate for use in resource-limited settings. 2022 Wiley-VCH GmbH. -
A social psychological investigation of Anti-nationalism in contemporary India A media analysis
The paper aims to explore the consensual and dissident social representations of anti-national that has re-entered the media lexicon in India post an alleged anti-national student protest in Jawaharlal Nehru University in the capital city of Delhi, India on February 9, 2016. The Social Representation Theory (SRT) is used to analyze the selected media reports of six daily newspapers, three each in English and Hindi language for a period of one year subsequent to the event. Results of thematic anchoring and anchoring by antinomy, a methodology used by Birgitta Hjer, are reported. The results reveal how the intergroup tensions and ideological differences in the polity constitute the conflicting social thinking on the referent. The paper has global implication for opening the possibilities of negotiation and transformation of social meanings related to nationalism and anti-nationalism. The Author(s) 2025. -
A Software Package for Detecting Anomalies in User Authentication
Anomaly detection is a very important tool for various applications such as intrusion detection, fraud, malfunction, system health monitoring and event detection in IoT devices. Recently, user authentication has become an extremely popular topic in information security research environments. The definition of user authentication is formulated as the process of verifying the identity declared by the user for a system object. Authentication is a method used to distinguish between true or false authentication requests. There are many methods used to authenticate a user that can identify valid users in protected resources. This article discusses various methods for analyzing abnormal user behavior in information systems, namely such methods as machine learning, neural networks, hybrid methods. Based on the analysis of system logs in the Astra Linux operating system, a software package has been developed to identify anomalies when trying to authenticate users. 2025 IEEE. -
A space for the space theorist remembering henri lefebvre
Despite being somewhat ignored in Indian academia, Henri Lefebvre comes to our rescue every time, helping us understand and respond to spacetime challenges. 2020 Economic and Political Weekly. All rights reserved. -
A Space Vector Modulated Direct Torque Control of Induction Motor with Improved Transient Performance and Reduced Parameters Dependency
Direct torque control (DTC) of induction motors is hampered by high torque and current ripple. Integrating DTC with space vector pulse width modulation (DTC-SVPWM) is one of the frequently used approaches to solve this problem. However, it adds to the computational complexity, increases the number of necessary motor parameters needed for control scheme implementation, and also affects the transient performance of the induction motor; this approach compromises the robustness and simplicity of DTC scheme. To get around these restrictions, a novel control strategy is put forth in this paper. The suggested scheme enhances the steady-state performance and transient response of the motor while preserving the simplicity and robustness of the DTC scheme. To accomplish this, the proposed control scheme operates at varying switching frequencies during transient conditions and constant switching frequencies during steady-state. The suggested speed control method does not employ any rotating reference frame transformations or usage of many rotor parameters for computation, nor does it call for sector identification and operates with a single PI controller. The suggested topology also uses a bus-clamped PWM modulation technique, which lowers the average switching frequency to 2/3 times the actual switching frequency. Thus, switching losses are also decreased. Simulation results show the effectiveness of the proposed topology in enhancing the transient and steady-state performance of the induction motor. The results are compared with the traditional DTC and DTC-SVPWM scheme. 2023 IEEE. -
A Spatio-temporal Model for the Analysis and Classification of Soil Using the IoT
The Internet of Things (IoT) is an evolving trend in the field of computer applications where various hardware and software are connected together to address a specific problem. With the help of the IoT, the world has become smart and enabled itself to connect various objects (e.g., cars, computers, mobile phones, and smart appliances) with distinctive Internet protocol addresses, which allows them to interact with one another, thus accomplishing various procedures. Applications of the IoT include but are not restricted to smart cities, healthcare, industry, and robotics. Amongst a huge list of applications furnished by the IoT, agricultural IoT is the theme of this chapter. The IoT in agriculture transforms entities such as crops, soils, and livestock in a smart way by utilizing underlying technologies such as embedded systems, pervasive computing, sensor networks, ubiquitous computing, ad hoc networks, various wireless communication technologies, Internet protocols and other advanced technologies. The research here focuses on the most important agriculture entity soil. It is the soil that determines the yield of a crop. The more fertile the soil, more qualitative is the yield. The main idea behind the research is to identify the soil most suitable for agriculture. Using a spatio-temporal model, the soil samples collected from various parts of the country are classified into agricultural soil and non-agricultural soil. This classification is done by the aid of features such as the pH of the soil, and its humidity, moisture, and temperature collected from IoT sensors. The chapter begins with an introduction to the usage of IoT technology in different areas of agriculture followed by an account of the proposed state-of-the-art model, and its results, analysis, and a conclusion. 2022 selection and editorial matter, Vikram Bali, Vishal Bhatnagar, Deepti Aggarwal, Shivani Bali, and Mario JosDiv; individual chapters, the contributors. -
A Specular Reflection Removal Technique in Cervigrams
Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcopes. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several prominent approaches for removal of specular reflections proposes a novel method to remove the specular reflections. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. 2023 IEEE. -
A Spiking Neural Network Approach to Electroencephalography based Consumer Preference Modeling
Neuromarketing is an emerging interdisciplinary field that applies neuropsychology in marketing to study consumer sensory-motor actions such as cognitive and affective responses to marketing stimuli through Brain Computer Interface (BCI) technology. While marketers spend over 750 billion dollars annually on traditional marketing procedures such as surveys, interviews, and consumers feedback, these methods are often criticized for their inability to capture genuine consumer preferences. Neuromarketing promises to overcome such issues by analyzing neural responses directly. This paper presents a novel framework for predicting consumer preferences by analyzing Electroencephalography (EEG) signals. EEG signals are acquired from 25 volunteers while administering 14 products with three different variations. The EEG signals are preprocessed using Modified Wavelet Thresholding (MWT) to remove noise while preserving neural activity patterns. A third-generation network, Spiking Neural Network (SNN) is designed to recognize consumer preferences based on EEG frequency bands. Unlike conventional models, SNN captures temporal dynamics through spike timing, which is crucial for EEG signals. The efficacy of the model is tested across individual EEG bands to identify the most influential frequency band in decision-making. Simulation outcomes demonstrate that the proposed model can effectively predict consumer preferences. The model achieved an accuracy of 90.91%, recall of 90.7%, a precision of 91.14%, a specificity of 91.12%, and an F1-score of 90.92%. The outcomes highlight the potential of EEG based neuromarketing systems to decode subconscious consumer responses, enabling brands and businesses to design more targeted marketing strategies based on objective neural data. 2025 Inventive Research Organization. -
A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data
Demand forecasting is crucial in the business sector. Despite the inherent uncertainty of the future, it is essential for any firm to be able to accurately predict the market for both short- and long-term planning in order to place itself in a profitable position. The proposed approach focus on the passenger transport sector because it is particularly vulnerable to fluctuations in consumer demand for perishable commodities. At every stage of the planning process from initial network designs to final pricing of inventory for each vehicle in a route-an accurate prediction of demand is essential. Forecasting passenger demand is crucial since passenger transportation is responsible for a substantial chunk of global commerce. The suggested method relies on three distinct techniques: data preparation, feature selection, and model training. Data modification, cleansing, and reduction are the three sub-processes that make up preprocessing. When it comes to feature selection, partition-based clustering algorithms like k-means are the norm. Let's go on to training the models with stacked BiLSTM. The proposed method is demonstrably superior to both LSTM and BiLSTM, the two most common competing approaches. The proposed method had a success rate of 98.45 percent. 2023 IEEE. -
A stakeholder theory approach to analysing strategies for improving pandemic vaccine supply chain performance
This study aims to formulate strategies that impact the vaccine supply chain (VSC). This study measures the VSC performance using the proposed strategy concerning stakeholders theory. From the literature review and experts consent, the strategies are classified into six broad strategies as-VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory. A questionnaire is developed for surveying healthcare organisations and hospitals. All six proposed hypotheses got accepted. The developed model satisfies all the model fit parameters. Strategies like VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory have positively impacted vaccine supply chain performance. This research will be helpful for healthcare professionals and organisations for the faster delivery of the vaccine. This research will also help policymakers in improving the performance of VSC. This study is also the first to use the stakeholder theory approach for measuring VSC performance. Copyright 2024 Inderscience Enterprises Ltd. -
A Statistical Analysis and Comparison of the spread of Swine Flu and COVID-19 in India
Introduction: The world is currently experiencing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [COVID-19], however, this is not a new phenomenon; it occurred in 2009-2010 in the form of novel influenza A. (H1N1). The H1N1 virus primarily afflicted people between the ages of 26 and 50, but SARS-CoV-2 primarily afflicted those over the age of 60, increasing the number of deaths owing to their weakened immunity. The report provides a case study of the impact of H1N1 and SARS-CoV-2 in India. Methods: Data is obtained from The Hindustan Times newspaper, GoI press releases and World Health Organization (WHO) reports. Results: The incidence rate was initially low and it was only by the 10-15th week that it started increasing. There is an initial upward trend before levelling out followed by a second wave and third wave. COVID-19 exhibited a steeper growth, where the steps taken by the Government were ineffective leading to higher death cases. Kerala was affected due to the travellers returning from the Middle East, while Maharashtra and Delhi saw large incidence rates due to the migrant influx and communal gathering. Conclusion: The most effective and practical approach is to test the symptomatic patients and aggressive testing to contain the transmission. Awareness campaigns to educate the public about social distancing and personal hygiene is more practical. There is still scope of improvement with regards to the public health care support, preparedness and response. Lockdown measures could have been avoided if the initial screening was conducted properly. 2022 UPM Press. All rights reserved. -
A statistical analysis of the stochastic drift between sensex & nifty - An in-depth study /
International Journal Of Innovative Research & Development, Vol.4, Issue 5, pp.208-212, ISSN No: 2278-0211 (Online) -
A statistical approach to study anatomical changes of pink guava cultivar (Psidium guajava L. cv Arka Kiran) during its ripening at the room temperature storage
The ripening of climacteric fruit like guava is a complex process that is highly coordinated with its cellular backbone. In the present study, we combined microscopy, spectrophotometry, and statistical analysis to evaluate the anatomical changes in the pink variety of guava during five ripening stages (pre-ripe, ripe, color-turn, half over-ripe, and over-ripe) during its storage at room temperature (282 C). The cholorophyll content of the peel, as determined by the measurement of chlorophyll a, b, and total chlorophyll, showed a significant decrease during the maturation process (4.05, 4.53, and 8.62 ?g/cm2, respectively, in the pre-ripe stage to not detectable in the over-ripe stage). Gradual loss of integrity of the fruit pulp (pericarp) from the preserved bee-hive structure to cell mass was also monitored by studying the cellular anatomy with brightfield and scanning electron microscopy. The epidermal thickness and width of the cortical parenchyma cells revealed statistical differences from the initial pre-ripe stage to the final full-ripe stage. Finally, based on the cellular dimensions, multivariate analysis using PCA (Principal Component Analysis) tool grouped the stages into three clusters, namely, pre-ripe: ripe, color-turn: half-over ripe, and over-ripe stages. In conclusion, this study provided significant insights into cultivar-specific anatomical changes in guava fruit, with potential for future research to develop variants with longer post-harvest storage life. 2024 The Author(s) -
A statistical approach to study anatomical changes of pink guava cultivar (Psidium guajava L. cv Arka Kiran) during its ripening at the room temperature storage
The ripening of climacteric fruit like guava is a complex process that is highly coordinated with its cellular backbone. In the present study, we combined microscopy, spectrophotometry, and statistical analysis to evaluate the anatomical changes in the pink variety of guava during five ripening stages (pre-ripe, ripe, color-turn, half over-ripe, and over-ripe) during its storage at room temperature (282 C). The cholorophyll content of the peel, as determined by the measurement of chlorophyll a, b, and total chlorophyll, showed a significant decrease during the maturation process (4.05, 4.53, and 8.62 ?g/cm2, respectively, in the pre-ripe stage to not detectable in the over-ripe stage). Gradual loss of integrity of the fruit pulp (pericarp) from the preserved bee-hive structure to cell mass was also monitored by studying the cellular anatomy with brightfield and scanning electron microscopy. The epidermal thickness and width of the cortical parenchyma cells revealed statistical differences from the initial pre-ripe stage to the final full-ripe stage. Finally, based on the cellular dimensions, multivariate analysis using PCA (Principal Component Analysis) tool grouped the stages into three clusters, namely, pre-ripe: ripe, color-turn: half-over ripe, and over-ripe stages. In conclusion, this study provided significant insights into cultivar-specific anatomical changes in guava fruit, with potential for future research to develop variants with longer post-harvest storage life. 2024 The Author(s) -
A Statistical Search for Star-Planet Interaction in the Ultraviolet Using GALEX
Most (?82%) of the over 4000 confirmed exoplanets known today orbit very close to their host stars, within 0.5 au. Planets at such small orbital distances can result in significant interactions with their host stars, which can induce increased activity levels in them. In this work, we have searched for statistical evidence for star-planet interactions in the ultraviolet (UV) using the largest sample of 1355 Galaxy Evolution Explorer (GALEX) detected host stars with confirmed exoplanets and making use of the improved host-star parameters from Gaia DR2. From our analysis, we do not find any significant correlation between the UV activity of the host stars and their planetary properties. We further compared the UV properties of planet host stars to that of chromospherically active stars from the RAdial Velocity Experiment (RAVE) survey. Our results indicate that the enhancement in chromospheric activity of host stars due to star-planet interactions may not be significant enough to reflect in their near- and far-UV broadband flux. 2020. The American Astronomical Society. All rights reserved.. -
A statistically guided hybrid machine learning framework for predicting supply chain resilience in complex operational environments
This study proposes a hybrid machine learning framework to predict supply chain resilience by integrating principal component analysis, K-Means clustering, and ensemble learning models. The approach captures firm-level heterogeneity, enabling context-specific resilience prediction and interpretability using SHAP values. The findings demonstrate that ensemble models, particularly XGBoost, outperform traditional regression models, and reveal distinct resilience drivers across operational clusters. The framework offers actionable insights for improving resilience strategies and contributes a scalable, explainable approach for data-driven supply chain risk management. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.