Browse Items (11810 total)
Sort by:
-
A fuzzy soft coronavirus alarm model
The entire world experienced a rampant outbreak of Covid-19 beginning in December 2019. The spread of this disease was so rapid and aggressive that many developed countries struggled to control it. However, some countries such as China and Australia have done a commendable job of controlling this virus. Various studies have been done in parallel to analyze strategies to curb the spread of the virus. In many locations, people displayed swarm intelligence. The collective behavior of people was mixed. Some people followed the instructions of the health authorities. In addition to the instructions, people in some localities developed self-organization to resist the spreading of the virus. This research work mainly focuses on the prediction of coronavirus spread in different districts of Kerala by use of a fuzzy approach as the fuzzy approach is considered the best tool that would not show imprecise data in any situation. The PRONE (Predicted Risk of New Event) indexing algorithm was used for finding the intensity of the spread in five districts of Kerala (Trivandrum, Ernakulam, Kozhikode, Kannur, and Kasargod) and was evaluated under the input parameters of immunity of person, food habits, financial factors, and age with the total number of infected people as the output variable. An eight-step algorithm is provided to determine the PRONE index. Kasargod is more vulnerable to the virus. The final results show that this proposed model better predicts virus spread. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A modified fuzzy approach to prioritize project activities
Project management is an important task in business although project is not just confined to business. Due to the uncertainty of the various variables involved in a project, over several past decades research is going on in the search for an efficient project management model. Although numerous crisp models are easily implementable, the potential of fuzzy models are huge. In the case of software development, the variables involved are highly dynamic. In this paper, we propose a ranking based fuzzy model that can prioritize various activities. We use a popular crisp model to test the effectiveness of the fuzzy model proposed. Simulation is done through Java Server Pages (JSP). There is considerable computational and managerial advantage in implementing the fuzzy model. 2018 Authors. -
A fuzzy computing software quality model
Expectation of the quality of a software varies from user to user. A fuzzy approach to measure the quality of a software is very appropriate so that it can deal with non-crisp aspects of the various parameters. In the proposed model, ordered intuitionistic fuzzy soft sets (OIFSS) and relative similarity measures of OIFSS are considered in the backdrop of fuzzy multiple criteria decision making (FMCDM) approach. 2019 Author(s). -
An ordered ideal intuitionistic fuzzy software quality model
Software is one of the major factors in the development of computer - based systems and products. Measurement of the software quality is thus the key factor that has to be taken into account while developing a software system. Many software quality models with numerous quality parameters are under use to measure the performance of a software system, on the basis of which the software is valued. This study intends to make available a fuzzy multiple criteria decision making (FMCDM) approach to measure software quality and to propose new similarity measures between ordered ideal intuitionistic fuzzy sets (OIIFSs). The proposed model is applied to five live software projects so as to quantify the software quality of each project under fuzzy environment. IAEME Publication. -
A fuzzy approach to project team selection
Project team selection is a complex process in software engineering. The study uses a multiple criteria decision making (MCDM) approach for the selection of a project team under fuzzy environment. In this paper a FRI, FSS approaches are developed to the selection of project team. 2019, International Journal of Scientific and Technology Research. All rights reserved. -
A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems
An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes. 2024 IEEE. -
Indigenous Beliefs and Practices for Sustainability Among the Mao Nagas
The present society of Mao Nagas is sandwiched between trends to modernity and tendencies to be rooted in the cultural past. Prior to the arrival of Christianity, the Maos were considered animists; the sway of the one Supreme Being, and human relations with nature permeated the social, cultural, and spiritual realm. When the sky represented the father, and the earth, the mother; exploitation becomes inconsequential. Despite the odds of having limited ancestral land, the Maos have proven themselves self-sustainable within the place of habitation. The fact that there are no beggars among the Maos proves that certain aspects of the SDGs are ingrained in the beliefs. The Feast of Merit prevented extreme riches in society. With education, the Mao Nagas learned the harmful effects of shifting cultivation and abandoned its entirety. This paper tries to conceptually prove that if ancient beliefs and practices are tempered with scientific knowledge, life is sustainable. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Reshaping the Education Sector of Manipur Through Blockchain
The use of technology in education has been over a century, yet blockchain is in its nascent stage in education. Over the years, technology has enhanced the teaching-learning method, and blockchain can improve even in the administrative section of education. The states of North East, India, in general, lag behind the rest of Indian states in almost all sectors, and the lack of transparency in the administrative sector significantly contributed to it. If blockchain is incorporated into the education department at the administrative level, it could pave the way for a faster, more transparent, and smoother administration. Given the harsh reality that transportation is hard and expensive, a standardised blockchain can alleviate the need to be physically present for any academic-related activity. The attempt of this study would be to show how blockchain can be beneficially used even at the institutional level, where unabated printing could be reduced and adopting to e-paper be maximised. Besides the educational institutions, the administrative sector in education could profitably use them in offices, thereby avoiding red tape for the common people. The chapter points out how blockchain can be a trailblazer in reshaping the education sector in Manipur. Educational institutions must take the lead towards a sustainable future, and blockchain can aid in bringing some visible change in the educational sector. This chapter uses an interdisciplinary approach to substantiate the importance and need for blockchain in the context of Manipur to change for a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Critical Estimation of CO2Emission Towards Designing a Framework Using BlockChain Technology
The automobile industry is a significant global contributor of carbon footprint this industry has impacted climate change, the research explores the existing methods of carbon footprint tracking and creates a framework by applying blockchain technology by connecting all the countries into one system as blockchain carries the capability to do due to its transparency, security and immutability the proposes of decentralised framework for real time tracking quarterly and implementing the necessary policies to mitigate the raising emission. The methodology encompasses of data analysis of using time series analysis globally and focusing certain parts of the world to show the emissions and creating a design that can help us in tracking the carbon footprint making all over the countries around to participate in suggesting to create a pathway for the future generations a better world as advance technologies come into the world for better ways to save the environment. 2024 IEEE. -
Confrontations faced by women in higher education institutions and strategies to overcome the anomalies in the mid-career
Women do succeed in higher positions in the higher education system but only to a certain point, and many women are really motivated by the traditional academic values such as passion to the discipline, pursuit of knowledge, good working environment, and flexibility. Women in higher education .spend the majority of their life at the mid-career stage. Some of them feel wedged, undervalued, and find no motivation to go forward in their mid-career. Hence, the mid-career stage is very much important with women academicians, and they feel there is little support or mentoring. Hence, the mid-career period is increasingly difficult to navigate. Women encounter enormous obstacles in their academic career, including unequal task distribution and balancing caring responsibilities to name a few. The aim of this chapter is to discuss in detail the challenges and obstacles faced by women in their mid-career in higher education and a few strategies to overcome the encounters. 2022, IGI Global. All rights reserved. -
Generative AI and its impact on creative thinking abilities in higher education institutions
Generative AI technologies such as ChatGPT have started gaining increased popularity among higher education institutions. Students, as well as teaching professionals, can utilize these tools for various academic purposes due to the immense benefits they provide by way of customization of data generated and ease of access to data. However, this chapter seeks to analyze how such tools may impact students' creative thinking ability. It also analyses the drawbacks faced by teachers after implementation of such tools. The methodology adopted for the study was two surveys: one administered to gather students' opinions and the other for understanding teachers' perspectives. The analysis of the data collected shows that the over-reliance of students on such generative AI tools might hinder students' ability to think creatively to some extent. The chapter also suggests some of the strategies that can be adopted by teachers to ensure students' capabilities are assessed accurately. 2024, IGI Global. All rights reserved. -
COVID-19 outbreak prediction using quantum neural networks
Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
An IoT-based agriculture maintenance using pervasive computing with machine learning technique
Purpose: In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc. In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach: In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings: In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like Threshold segmentation and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value: The implemented machine learning design is outperformance methodology, and they are proving good application detection rate. 2021, Emerald Publishing Limited. -
AGRI 4.0 AND THE FUTURE OF CYBER-PHYSICAL AGRICULTURAL SYSTEMS
Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with a focus on technologies that enable the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery, and consumption. Specific sections explore ways to implement CPS effectively and appropriately and cover digitalization of agriculture, digital computers to assist the processes of agriculture with digitized data and allied technologies, including AI, Computer Vision, Big data, Block chain, and IoT. Other sections cover Agri 4.0 and how it can digitalize, estimate, plan, predict, and produce the optimum agricultural inputs and outputs required for commercial purposes. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices, and government policies in Agri 4.0. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Data Science in the Medical Field
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 20072022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved. -
Relationships between Ultrasonographic Placental Thickness in the Third Trimester and Foetal Outcomes
Poor neonatal outcomes, including low birth weight (LBW), poor APGAR scores, more NICU hospitalizations, and a higher chance to develop Pre-Eclampsia, IUGR, and Oligo Hydramnios, are all linked to thin placental thickness. While both thin and thick placentae are connected to a greater prevalence of C-sections, thick placentae are linked with a greater possibility of developing GDM and an increase in NICU hospitalizations. Objective of this research was to investigate the association between placental thickness as measured by ultrasonography in the third trimester and foetal outcome, including the relationship between placental histopathology and placental thickness. investigate the link among placental thickness, foetal outcome, and placental histology. Most newborns had fibrinoid necrosis and calcifications. Babies with Macrosomia and IUGR, respectively, were more likely to develop Syncytial knots and thickening of the vessel wall. Patients with normal placenta thickness at 36 weeks' gestation experienced fewer difficulties than those with thin or thick placentas at the same time. The study emphasizes the value of evaluating placental thickness using ultrasound in the third trimester to detect high-risk pregnancies. The study also shows that aberrant foetal and neonatal events are linked to certain placental histological characteristics, like artery wall thickening and infarctions. RJPT All right reserved. -
Single Port Multimode Reconfigurable UWB-NB Antenna for Cognitive Radio Applications
In this paper, a compact, single port, multimode reconfigurable UWB-NB antenna with a novel feeding network is presented. The proposed antenna consists of a pentagonal-shaped monopole radiator, a beveled-shaped partial defected ground plane with a rectangular slot, and a reconfigurable bypass feeding network. The antenna realizes a wideband frequency range from 2.4 to 18 GHz and four narrow band frequency ranges, 5.3 to 6.8 GHz, 6.0 to 7.6 GHz, 7.2 to 8.8 GHz and 8.4 to 11.4 GHz. The antenna provides an omnidirectional radiation pattern with gain from 2.2 to 6.2 dBi maximum at 12 GHz and voltage standing wave ratio (VSWR) ranges from 1 to 2. The fabricated antenna has an overall dimension of 181.6 mm3. Sensing and tuning ranges of the fabricated antenna shows good agreement with the simulation results. The proposed antenna has an advantage of simple design, low profile, single port excitation and omnidirectional radiation pattern making it suitable for applications such as handheld mobile cognitive radio systems. 2022 SBMO/SBMag -
A Compact Super Wideband Antenna with Controllable Dual Notch Band Capability
In this paper, a novel super wideband (SWB) antenna with dual band notch capability is designed and analyzed for wide band applications. The proposed antenna consists of a pentagonal shaped radiator, beveled-shaped partial ground plane with slot and U-shaped parasitic strips. The beveled-shaped defected ground structure with rectangular slot helps to realize wideband characteristics from 2.4 to 28.2 GHz. Independent control of the notch band's center frequency and bandwidth is achieved by using U-shaped parasitic strips. This key feature is achieved in the WiMAX (3.3 to 3.7 GHz) and WLAN (5.1 to 5.9 GHz) bands. Furthermore, it exhibits a stable radiation pattern and offers acceptable gain over the entire operating bandwidth with sharp decrease in gain at the notches. The percentage bandwidth of 169% is achieved with a bandwidth dimension ratio (BDR) of 6986. Group delay is less than 1 ns in the entire operating bandwidth except at the notch bands. The measured reflection and radiation characteristics of fabricated SWB antenna are in good agreement with the simulation results. The proposed antenna has the advantage of simple design and compact size with an overall dimension of 18 x 21 x 1.6 mm3. The performance of the proposed antenna is superior compared to reported antenna designs in terms of controllable sharp notches and size for the bandwidth achieved. 2022 IAMOT -
