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Scientific basis for the preparation and characterization of iron based traditional drug annabhedi sindooram: A materialistic approach
Iron based traditional Ayurvedic drug Annabhedi Sindooram is used therapeutically for the treatment of diseases like Anaemia, Leucoderma, Prolapse of rectum and uterus, Spleenic disorders. The preparation method of iron based Indian traditional drug Annabhedi Sindooram involves conversion of a pure metal into its mixed oxide by drying and incineration. Commercially available ferrous sulphate is used as the source of iron for the preparation of Annabhedi. The structural and textural properties of the starting materials and the prepared drug were characterized systematically by different characterization techniques like PXRD, Zeta Potential Analysis, particle analysis, FTIR, ICP -AES, SEM and BET surface area analysis. The results obtained by characterization of the samples clearly explain the formation of Fe2O3, reduction in particle size, modification of surface energy and formation of metal complex with organic moieties. The strict post and pre preparation conditions followed play an important role in the morphology and medicinal activity of the drug Annabhedi Sindooram. -
Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles
Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
SarNet-1 -A Novel Architecture for Diagnosing Covid-19 Pneumonia and Pneumonia through Chest X-Ray Images
Coronavirus (COVID-19) is a contagious disease which begins with flu-like symptoms. COVID-19 arose in China and it rapidly spread throughout the globe, leading to a pandemic. For many, it was noticed that the infection started with fever, cough and finally leading to pneumonia. It is very necessary to differentiate between covid pneumonia and general pneumonia for appropriate treatment. Chest X-ray readings are useful for radiologists to identify the severity of infection. While computerising this mechanism, deep learning techniques are found to be very useful in extracting relevant features from medical images. This can help in differentiating pneumonia, COVID19 pneumonia and x-rays of a healthy person. Computer aided methods for identifying the presence of pneumonia can help health providers to a great extent for quick diagnosis. The X-rays gathered from freely available datasets are used in this work to propose an architecture for categorising X-rays into pneumonia and covid pneumonia. 2022 International Journal on Recent and Innovation Trends in Computing and Communication. All rights reserved. -
Yulu: Moving Towards a Sustainable Future
The rapidly rising rate of urbanization, which is closely linked to economic growth, has exposed the world to several challenges such as inequality, environmental degradation, traffic congestion, infrastructural concerns and social conflicts. Therefore, urban sustainability has emerged as one of the most debatable discussions across the world. The existing network of transportation can no longer keep up with the growing demand in metropolitan cities. Short distance travel has become an unresolved issue for daily commuters. The case presents how MMVs have emerged as an alternative mode of transport for resolving issues of daily commuters regarding the first-mile connectivity, last-mile connectivity and short distance travel to reach their final destination. MMVs are basically light-weight vehicles which occupy less space on road. These vehicles include bicycles, e-bikes, skateboards, hoverboards and other battery-operated vehicles. The case narrates the journey of Yulu, a dockless bike-sharing venture which promoted the concept of green consumerism among the daily commuters at affordable rates. The venture initially started in the IT city of Bangalore and later expanded its operations to other cities such as Pune, Navi Mumbai, Gurugram and Bhubaneswar. The speciality of this venture is that it offers a sustainable solution to ever-increasing problems of traffic congestion and aggravating air pollution issues in metropolitan cities. Dilemma: How to offer a sustainable solution to the ever-increasing problem of traffic congestion and aggravating air pollution due to rising vehicular traffic? How to make short distance travel affordable and more convenient for daily commuters? Theory: Three pillars of sustainable development. Type of Case: Problem solving applied case. Protagonist: Present. Discussion and Case Questions: What strategies should be employed by the start-up to make it a more popular form of commute? How can the increasing rate of damage to the vehicles be brought down? How does organization structure and cluster management practices of Yulu help it to become more sustainable? How can the regulatory bodies and government promote and adopt such start-ups in their urban planning projects? 2020 SAGE Publications. -
The Belt and Road Initiative: Issues and Future Trends
The Belt and Road Initiative (BRI) is a China-led plan that involves infrastructure and construction projects in more than 140 countries, out of which 65 countries account for 30% of the worlds gross domestic product, 35% of the worlds trade, 39% of the global land, 64% of the worlds population, 54% of the worlds CO2 emissions and 50% of the worlds energy consumption (Du & Zhang, 2018, China Economic Review, 47, 189205). The project announced in 2013 is often considered Chinese Premier Xi Jinpings dream. It quickly grew in sectoral and geographical complexity from the Arctic to deep oceans, to Latin American countries, Africa and even collaborations in maritime and outer space. Nine years into the making, the project suffered disruption in the wake of the COVID-19 pandemic. Travel restrictions and lockdowns led to suspension and slowdown in the project. However, the Chinese leadership continues to remain optimistic regarding the BRI and is opting for digital, health and sustainability models to keep the initiative running. The article analyses the strategic and economic significance of the BRI from its inception to now. It focuses on the impact of the pandemic on the BRI and stakeholders responses to the project, and looks into attempts by China to make it a success in the post-pandemic world. 2023 Indian Council of World Affairs(ICWA). -
Covid-19 and Quad's Soft Reorientation
Quadrilateral Security Dialogue comprises a group of countries the US, Japan, Australia, and India, that started maritime collaboration in the wake of the 2004 Indian Ocean Tsunami. The initiative lasted for a brief period before falling apart in 2008. The countries re-banded together in 2017 to consult on ensuring greater security and prosperity in a free and open Indo-Pacific region, and a rules-based order. During the Covid-19 pandemic, the group has been partnering on soft security aspects such as vaccine development and distribution. The paper suggests that this allows the group to become first movers in the areas of specific functional challenges. This paper looks at the role of health diplomacy in the region as a soft power tool. The theory is based on the works of Professor Joseph Nye who first coined the term 'soft power'. It focuses on the role of India in strategic altruism to enhance Quad's strategic influence in the region. Expanding global vaccine supply is an example of reaching out to low- and middle-income countries. The paper argues that enhancing such cooperative mechanisms will allow Quad to balance its cooperative and competitive outlook in the region, linking its security with prosperity and development objectives. 2021 -
Exploring the Role of structurally modified Molybdenum disulfide composites with Prussian blue analogues as counter electrode catalysts for bifacial Dye-Sensitized solar cells
The present study aims to utilize Mn, Ni, and MnNi Prussian Blue Analogue (PBA) embedded MoS2 composites as Pt-free Counter Electrode (CE) in Dye Sensitized Solar Cells (DSSCs). Therefore, Ni-PBA, Mn-PBA, and MnNi-PBA were synthesized using a simple ageing procedure followed by a Hydrothermal method to prepare modified MoS2 composites. The crystalline structure, shape, surface area, and elemental oxidation state were analyzed using various studies. Also, the nanosheets formation around cubic structure further shows large numbers of active sites resulting in the high catalytic behaviour of the composites. Among the various composites, the Modified MoS2 based on MnNi-PBA, which was coated using a simple spin-coating procedure, exhibited the smallest ?EPP separation and the highest JRED value due to the rapid redox reaction at the CE/electrolyte interface and catalytic current. The maximum efficiency of 8.25 % was achieved for MnNi-PBA based composites, surpassing pristine MoS2 (6.72 %) and Pt (7.58 %) under front illumination (100 mW/cm2). Under rear illumination, the cell demonstrated a higher efficiency of 4.96 %, attributed to the high transmittance of the material-coated CE, making it suitable for bifacial applications. 2024 International Solar Energy Society -
Phosphorus-doped molybdenum disulfide as counter electrode catalyst for efficient bifacial dye-sensitized solar cells
MoS2 is a promising counter electrode material for dye-sensitized solar cell owing to its optical and electrical properties and two-dimensional layered structure. However, it still suffers from minimal conductivity, poor charge transport and less active sites. The present study offers a promising method for enhancing catalytic and fast charge transfer in MoS2 through heteroatom doping of phosphorus. A facile one-step hydrothermal treatment was acquired to do the phosphorus doping. The spin-coated P-doped MoS2 (MSP2) counter electrode (CE) shows a superior power conversion efficiency of 7.93% for front illumination and 5.34% for rear illumination, outperforming Pt-based (7.41% and 5.75%) CE. Thus, phosphorous incorporation increases the number of active sites and improves the catalytic property of the material. The P-doped MoS2 (MSP2) CE film also shows high transmittance, making it a suitable choice for bifacial type of solar cell. 2023 Elsevier Ltd -
Beyond the first bite: understanding how online experience shapes user loyalty in the mobile food app market
In the competitive landscape of mobile food ordering applications (MFOA) in India, the primary focus is enhancing the customer experience to mirror or even exceed their offline meal acquisition experiences. Existing research underscores the pivotal role of a superior online experience in driving business success. Against the backdrop of a dearth of studies addressing online customer experience (OCE), our current research seeks to gain insight into its state and its implications for attitudes and intentions. Specifically, we investigate the impact of OCE on the continued usage intentions (CUI) of new MFOA users. This study not only sheds light on the relationship between OCE and CUI but also presents a fresh configuration of OCE, addressing its varied conceptualization. Furthermore, drawing on data collected from over 400 first-time users of MFOA, our findings reveal that e-satisfaction and e-trust act as full mediators in influencing CUI. Finally, the study also suggests that e-trust mediates the effect of e-satisfaction on the CUI of MFOA users. Our research contributes to our understanding of OCE by specifically highlighting the experiences and outcomes of first-time users of MFOAs. Practitioners should employ strategies including personalized orientation and data gathering, location-based services, in-app messaging, push notifications and gamification techniques to increase OCE and drive CUI. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024. -
Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975. 2022 River Publishers. -
Machine learningbased approaches for enhancing human resource management using automated employee performance prediction systems
Purpose: This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach: In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employees performance meets expectations or needs improvement. Findings: All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications: The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the models effectiveness across various contexts. Practical implications: The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications: Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value: This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach. 2024, Emerald Publishing Limited. -
Wave Height Forecasting over Ocean of Things Based on Machine Learning Techniques: An Application for Ocean Renewable Energy Generation
With the evolution and integration of information and communication technologies, the marine environment is being converted into a smart ocean of things. The only way to monitor the marine environment is to access marine information through satellites, radar, etc. Recently, many researchers have focused their interest on generating power from renewable energy. Among all the available renewable resources, ocean waves are attracting the interest of researchers for power generation. Therefore, this article focuses on designing a data-driven forecasting model for marine renewable energy generation applications. This article applies a novel Gini-impurity-index-based bidirectional long short-term memory model for selecting the best ocean/marine environmental factors to forecast wave height and ultimately predict power generation using the numerical model. This article presents short- and long-term forecasting results. In the experiment, four stations each are taken for both short- and long-term forecasting. The average root-mean-square error was approximately 0.17 for long-term forecasting and approximately 0.05 for short-term forecasting. 1976-2012 IEEE. -
Investigation on the analysis of integration of IoT and AI technologies with information security for advanced education 4.0
This research examines the integration of emerging technologies in the form of the Internet of Things and Artificial Intelligence in driving forward to the educational application of Education 4.0. The systematic meta-analysis study provides evidence in the transformative capability of these technologies regarding attendance, performance, and learning pathway. The systems implementation was in the form of IoT sensors to capture and record student attendance, while the use of Artificial Intelligence based on machine learning models such as Support Vector Machine, Artificial Neural Network, k-Nearest Neighbors, and Decision Tree generated a personalized recommendation for the academic improvement or sports activity to be participated as an extracurricular activity. The performance evaluation of these models was illustrated for accuracy to correctly predict student responses related to the provided recommendations. The findings of implementation suggest the systems significant impacts given the augmented performance achievement with respect to academics and sports is the result of the implementation. It was measured comparing students performance before and after system implementation to capture the interpretation of student improvement regarding the use of the implemented system. The findings indicated that the systems implementation contributed to the increase in academic improvement from 65% to 75% and sports performance from 55% to 70% depending on student response to the provided academical or extracurricular recommendations. Such findings confirm an overall improvement in performance based on the use of the presented system. Taru Publications. -
Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection
In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care. 2024 River Publishers. -
Discrete financial in sentimental analysis using exploring patterns and trends
In todays rapidly evolving financial environment, its crucial for investors and decision-makers to effectively analyze stakeholder communications to gain valuable insights. This research conducts a comprehensive evaluation of a range of models that utilize machine learning, such as CNN (Convolutional Neural Network), LR (Logistic Regression), Doc2vec, and LSTM (Long Short-Term Memory), to determine their efficacy in interpreting investors sentiments and predicting business assessments and trading dynamics. The justification for preferring deep neural architectures compared to conventional data analysis lies in the challenge of handling extensive amounts of diverse and unorganized data. Deep learning techniques have shown impressive capacity in automatically detecting complex characteristics and unveiling concealed patterns within written records, rendering them well-suited for sentiment analysis in financial dialogue. This research questions the notion that depending exclusively on data from a solitary origin leads to persistently effective investment moves. In fact, stakeholder communication is impacted by numerous influential elements, leading to diverse sentiments and sentiments. Through our comparative assessment, we aim to illuminate how various deep learning models can adeptly capture the intricate nuances of sentiment within fiscal messaging. 2024, Taru Publications. All rights reserved. -
Vigilance and surveillance reinforced using mathematical approaches in object tracking techniques
Visual tracking is crucial to the study of object recognition and has been utilized in a variety of realistic settings, such as robotics, traffic monitoring, self-driving automobiles, forensics, and more. This research concentrates on techniques for counting the total number of individuals entering or exiting a space under the watchful eye of a camera. The techniques described here can detect the number of persons in a scene, both for a single individual and for many passers in front of the camera. With the aid of surveillance that use the centroid concept, an effective solution has been devised for monitoring. Secondly, in this study, object tracking methods utilising deep learning are also reviewed and analysed. This study also compares the effectiveness of various algorithms on the LaSOT, VOT2015, VOT2016, VOT2017 and OTB2015 tests. 2024, Taru Publications. All rights reserved. -
A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks
The past several decades of research into machine learning have been of great assistance to humanity in the diagnosis of a variety of ailments using various forms of automated diagnostic procedures. Machine learning, combined with smart health devices, has improved health monitoring, timely diagnoses, and treatment. This paper introduces a unified disease diagnosis framework, integrating cloud computing, machine learning, and IoT. The framework has three layers: physical (collects patient data), fog (intermediate layer with a domain identification unit to determine input and diagnosis type), and transmission (cloud server with a disease detection unit). The performance evaluation shows the robustness and efficiency of the model as compared to state-of-art models. 2023, Taru Publications. All rights reserved. -
Discovering patterns of live birth occurrence before in vitro fertilisation treatment using association rule mining
According to estimates, in-vitro fertilisation (IVF) is credited for the delivery of over 9 million children globally, constituting it to be a highly remarkable as well as commercialised advanced healthcare treatment. Nonetheless, the majority of IVF treatments are now constrained by factors such as expense, access and most notably, labour-intensive, technically demanding processes carried out by qualified professionals. Advancement is thus crucial to maintaining the IVF markets rapid growth while also streamlining current procedures. This might also improve access, cost, and effectiveness while also managing therapeutic time efficiently and at a reasonable cost. IVF has become a renowned technique for addressing problems like endometriosis, poor embryo development, hereditary diseases of the parents, issues with the biological function, problems with counteracting agents that harm either eggs or sperm, the limited capacity of semen to penetrate cervical bodily fluid, and lower sperm count that lead to infertility in humans. Copyright 2023 Inderscience Enterprises Ltd. -
Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection
Modern technology like Artificial Intelligence (AI) must be used in the agricultural sectorif sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases & pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Effect of glass and coir fiber on geotechnical properties of clayey soil
The use of fibers for the improvement of weak subgrade soils is beneficial as it not only acts as reinforcement but also, increases drainage, provides better workability, inexpensive and required in exiguous quantity. Available studies on clay soil reinforced are limited to a particular type of fiber, any comparative study on two or more types of fibers on same soil, provides a useful information on understanding suitability of specific type of fiber. This study deals with experimental characterization of clay soil reinforced with glass and coir fibers. California Bearing Ratio (CBR) and Unconfined Compressive Strength (UCS) tests were performed on these fiber reinforced clay samples with different percentage of glass and coir fibers. The results of these unreinforced and reinforced soils are compared. 2019 SERSC.