Showing papers in "ECS transactions in 2022"
TL;DR: In this paper , the authors investigate sustainable education and positive psychological interventions in schools towards achievement of sustainable happiness and well-being for 21st century pedagogy and curriculum, and recommend that happiness should be an aim of education and good education should contribute significantly towards personal and collective happiness.
Abstract: The aim of this paper is to investigate sustainable education and positive psychological interventions in schools towards achievement of sustainable happiness and well-being for 21st century pedagogy and curriculum. The growing awareness that sustainability, happiness, and well-being are intertwined takes the discussion of happiness and sustainability to newer levels. Even though scholars and governments are now grappling with questions about policies for sustainability, happiness, and well-being, the general public may not be aware of these connections and none of these topics are well integrated into formal education. Nevertheless, the necessity for transforming education to play a leading role in sustainable education has never been more imperative, and thus it has been taken up extensively in this paper. The concept of sustainable happiness offers an innovative perspective to reinvigorate sustainability education and shape priorities for 21st century learning: contributing to resilient, sustainable happiness and well-being for all. The education sector, however, is conservative and slow to adapt. The author recommends that happiness should be an aim of education, and a good education should contribute significantly towards personal and collective happiness. Broadening this recommendation to consider sustainable happiness and well-being for all is an overarching aim that could assist to reimagine the role of education in the 21st century and serve as the foundation for setting new priorities.
21 citations
TL;DR: The work in this article examines how transformative learning has been conceptualized and operationalized in education for sustainable development and sustainability learning, and gathers evidence on how to promote transformative learning in formal and non-formal settings.
Abstract: This research examines how transformative learning has been conceptualized and operationalized in education for sustainable development (ESD) and sustainability learning, and gathers evidence on how to promote transformative learning in formal and non-formal settings. The author performed a systematic literature review to create a bibliometric overview that combines a quantitative description of the body of literature with a qualitative study of the learning processes, results, and circumstances. The current investigation shows that transformative learning theory may help in designing and implementation of educational interventions and evaluations of learning towards sustainability by analyzing the learning process, results, and circumstances in the core sample of studies. This systematic review allows for a better understanding of how transformative learning theory’s concepts and mechanisms are operationalized in sustainability learning and ESD research, and it serves as a source of encouragement for researchers and practitioners working to make sustainability education, teaching, and learning more transformative.
20 citations
TL;DR: In this article , a single-junction solar cell by employing CIGS (Eg = 1.1 eV) material has been investigated, and the cell is optimized for maximum conversion efficiency (16.5%) by collectively varying the thickness of CIGs and bulk defect density using the SCAPS-1D tool.
Abstract: Copper indium gallium selenide (CIGS) is the most promising material owing to its low cost, superior optical properties, high performance, low-temperature coefficient, direct bandgap, long-term stability, and high absorption coefficient. Thus, here in this work, a single-junction solar cell by employing CIGS (Eg=1.1 eV) material has been investigated. Further, the CdS buffer layer is also used, which contributes to the p-n junction formation. The cell is optimized for maximum conversion efficiency (16.5%) by collectively varying the thickness of CIGS and bulk defect density using the SCAPS-1D tool.
14 citations
TL;DR: Comparing the radical segmentation of human iris using two different machine learning algorithms in an uncontrolled environment image dataset shows that proposed CNN shows better accuracy than SVM classifier in iris segmentation tasks.
Abstract: The main aim of this study is to compare the radical segmentation of human iris using two different machine learning algorithms in an uncontrolled environment image dataset. Materials and method: Images taken from MMU iris dataset, Convolutional Neural Network (CNN) model, and Support Vector Machine (SVM) model implemented to segment iris in uncontrolled environment image with 50 samples per group. Results: MATLAB simulation result shows that CNN has accuracy of 94% and SVM has 72% in segmenting iris. Attained significant accuracy (0.001) in SPSS statistical analysis. Conclusion: For the given images, proposed CNN shows better accuracy than SVM classifier in iris segmentation tasks.
13 citations
TL;DR: In this paper , tamarind seed powder particles were added to an epoxy resin matrix to test the material's tensile strength, which significantly increased the tensile properties of hybrid bio-composite boards.
Abstract: Nowadays, hybrid bio-composites are being developed by combining different natural resources as reinforcement and filler components, and this has raised their necessary qualities dramatically. Sugarcane fibre and tamarind seed powder particles added to an epoxy resin matrix to test the material's tensile strength were the focus of this study. A reinforcing material is sugarcane fibre, while filler components include tamarind seed powder particles. Different reinforcement and filler materials were used to make hybrid bio-composite specimens, while the epoxy resin weight percentage was maintained constant. Utilizing the hot press compression moulding technology, hybrid bio-composite boards were manufactured from start to finish. Water jet machining is used to remove hybrid bio-composite specimens for compression tests in accordance with ASTM standards from the hybrid bio-composite boards. It has been shown in experiments, for example, that adding tamarind seed powder particles to a sugarcane fiber/epoxy resin matrix considerably increases the hybrid bio-composites' tensile characteristics.
11 citations
TL;DR: Galvanic Skin Response signals are used for emotion detection and different machine learning models are used to classify the various emotional states with better accuracy, including k-Nearest Neighbors, Support Vector Machine, and Logistic Regression.
Abstract: In our day-to-day life, the proper perception of emotion plays an important role in human decision making and behavior. Nowadays, a lot of research is focused on the evocation and precise detection of human emotion, which can be later utilized in a different set of arena. There is good amount of research on emotion detection through parameters extracted via Face Recognition and Speech Modulation, etc. However, there is a huge question on the accuracy or effectiveness of these results as these features can be controlled or manipulated by the subject/person. So, the next approach is the usage of Physiological Signals. These signals are generated by the Central Nervous System (CNS) and cannot be controlled or manipulated by the subject/person. In the proposed work, we have used Galvanic Skin Response (GSR) signals for emotion detection. It is an easily available off-the-shelf, non-invasive sensing device, and is easy to use. We have used different machine learning models to classify the various emotional states with better accuracy. The different classifiers that are used are the k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Logistic Regression (LR).
10 citations
TL;DR: The point is to innovate with a device which creates messages on various stages to advice agriculturalists and help government in their ambitious target of doubling the agriculturalists income by 2022 of 2015 base levels.
Abstract: Internet of Things (IoT) is an innovation that reprimands the fate of correspondence and registering. In present times, IoT services are availed in multiple domains like smart homes, shrewd traffic control, urban planning, health infrastructure, and so forth. The utility domain of Internet of Things is quite vast and can be actualized in diverse fields. This paper work is about the execution of Internet of Things in agriculture. The latest advancements of farming with utilization of Internet of Things in agribusiness strategies can be cost effective for agriculturalists. It can help in decreasing the yield wastages and bumper crop yields and help in curbing the issues of hunger and malnutrition. Smart cultivating depending upon Internet of Things advances would help the cultivators and agriculturalists in diminishing the waste and in enhancing the profitability going from the amount of manure used within required technical quality norms. The point is to innovate with a device which creates messages on various stages to advice agriculturalists and help government in their ambitious target of doubling the agriculturalists income by 2022 of 2015 base levels. The item will help agriculturalists by getting live information from the farmland to find a way to empower them to do shrewd cultivating by likewise expanding their harvest yields and sustainable use of inputs. The proposal is a basic engineering of Internet of Things sensors that gather data and send it over the Wi-Fi system to the server. The server can take activities relying upon the data.
10 citations
TL;DR: In this paper , the first time demonstration of quintessential integration of Streptavidin magnetic nanoparticles and graphene oxide π-plasmons as crucial spacer material for augmented surface plasmon-coupled fluorescence (SPCF) was reported.
Abstract: Surface plasmon-coupled fluorescence (SPCF) technique has developed as a highly sensitive, specific, and versatile methodology for quantitative and qualitative bioassays. It relies on the ability of prism-coupling approach to channelize the emitted photons into sharply directional emission with attributes of propagating surface plasmons resulting in highly p-polarized emission. There are voluminous research studies demonstrating the utility of wide range of nanomaterials as effective spacers in SPCF. Streptavidin magnetic nanoparticles (SMNPs) serve as excellent candidates for biomedical applications on account of their biocompatibility and tunable magnetic properties. From the perspective of magneto-plasmonics, although they are excellent candidates for SPCF explorations, they are underutilized in this domain. In this background, we report the first-time demonstration of quintessential integration of SMNPs and graphene oxide π-plasmons as crucial spacer material for augmented SPCF. The results obtained with exorbitant detector systems are substituted with reliable mobile phone camera based detectors, amenable for resource-limited settings.
10 citations
TL;DR: The purpose is to quickly identify the best candidates for constructing heart disease prediction models, and to do it in a timely manner, and a thorough evaluation of the utility and consistency of data mining approaches found that CNNs performed best.
Abstract: Even in rural parts of many nations, coronary heart disease has emerged as the main cause of mortality. More than 23 million people will die from cardiovascular disease by 2030, according to the World Health Organization (WHO). With the use of cardiovascular disease prediction, healthcare practitioners may check the characteristics necessary for diagnosis, such as blood pressure and diabetes, which are vital. Many data mining methods are currently used in the medical industry, but additional research is needed to evaluate how well these categorization approaches function in real-world settings. The project's purpose is to quickly identify the best candidates for constructing heart disease prediction models, and to do it in a timely manner. The goal of this study is to increase the accuracy of cardiac disease prediction by addressing and overcoming the issues in the area (CVDs). CAD systems, which help physicians make choices, are often developed as a result of breakthroughs in machine learning technology. The categorization and prediction of cardiac disease are discussed in this article. The algorithms explored include ANN, KNN, and CNN. To conduct the evaluation, we utilised the UCI Cleveland database. For these algorithms, a thorough evaluation of the utility and consistency of data mining approaches found that CNNs performed best. Usefulness and consistency of data mining techniques for these algorithms revealed that the CNN was the most reliable process.
10 citations
TL;DR: An Elastic Timer Protocol for home automation system networks that performs better in terms of consumption of energy and time of convergence than other clock synchronization protocols and improves the elasticity and efficiency of the network, resulting in a faster network convergence time.
Abstract: Clock synchronization techniques are in highly demand for smart home applications with the approach of the Internet of Things (IoT). The developing innovation of smart home applications built on the Internet of Things is generally constrained and scattered. Synchronization is the basic characteristic of the emerging field of IoT in smart home applications which is recommended for improving the acceptance and its use. For the clock synchronization between systems, Network Time Protocol (NTP) is used. But due to inconsistent routing and limited computing resources there can be the dominant source of error that limits time transfer uncertainty. This paper proposes an Elastic Timer Protocol for home automation system networks that performs better in terms of consumption of energy and time of convergence than other clock synchronization protocols. The proposed protocol improves the elasticity and efficiency of the network, resulting in a faster network convergence time. Furthermore, simulation results revealed that the proposed work has improved performance metrics and proved superior performance characteristics when compared to standard synchronization protocols.
10 citations
TL;DR: The simple solution combustion method has been used for the synthesis of NiO nanoparticles using nickel nitrate as an oxidizer, Samanea saman pod extract as a fuel at 500°C as mentioned in this paper .
Abstract: The simple solution combustion method has been used for the synthesis of NiO nanoparticles using nickel nitrate as an oxidizer, Samanea saman pod extract as a fuel at 500°C. The crystalline nature of the nanoparticles was confirmed by X-ray diffraction studies and SEM images the information about surface morphology of the nickel oxide. The EDS spectrum confirms the composition and purity of the nanoparticles. Further, the nanoparticles are subjected for antimicrobial activity. The results revealed that NiO nanoparticles exhibit good antimicrobial properties.
TL;DR: In this paper , the density of states, band structure and quantum capacitance (QC) of bare and functionalized niobium carbide and titanium carbide MXenes are studied using DFT simulations.
Abstract: Supercapacitors in combination with renewable energy sources can provide environment friendly and sustainable energy devices. But due to their lower energy density, they are not able to replace batteries for all commercial applications. Their energy density can be increased by using high quantum capacitance electrodes. MXenes have potential to be used for such electrodes. In present work, density of states, band structure and quantum capacitance (QC) of bare and functionalized niobium carbide and titanium carbide MXenes are studied using DFT simulations. The calculated quantum capacitances of Nb2C and Ti2C show significant values of 324.1 and 246.2 µF/cm2 respectively. Nb2C shows higher value for QC in comparison to Ti2C for both positive as well as negative electrodes. These calculations are also performed for functionalized MXenes using =O, -F and –OH as functional group. Functionalization of MXenes with these groups reduces the QC of Nb2C and Ti2C MXenes at Fermi level.
TL;DR: The proposed CSSF-C LPF circuit is expected to improve the quality of acquired EEG signals and has been compared with Complementary Source Follower (CSF) LPF targeted for the detection of EEG signals.
Abstract: Design of a filter for biomedical application is a challenging task owing to its low-power and low-noise values introduced at low frequencies. This paper describes the design of a Complementary Super Source Follower (CSSF-C) Low Pass Filter (LPF) circuit and the same has been compared with Complementary Source Follower (CSF-C) LPF targeted for the detection of EEG signals. Simulated results obtained in Cadence Analog Design Environment using CMOS 0.18 µm technology node shows the CSSF-C LPF circuit emulates gain of -528 mdB, bandwidth of 100 Hz, power consumption of 12.6 nW from 0.5 V supply voltage. Further, the proposed LPF circuit is capable of achieving Dynamic Range (DR) of 63 dB with an Input Referred Noise (IRN) of 36 µVrms. The proposed CSSF-C LPF is expected to improve the quality of acquired EEG signals.
TL;DR: In this paper , the authors provide an insight into various machine learning techniques used for precision agriculture using WSNs, which can be applied to precision agriculture to increase crop growth, manage the process of crop cultivation, and create a perfect environment for the crops to increase productivity with less human effort.
Abstract: In India, approximately 70% of the total population is dependent on agriculture for their livelihood. Hence, it is essential to pay attention to agriculture to increase crop quality and quantity, thus increasing the overall cultivation yield. The traditional methods used require a lot of farmer’s effort and hard work, which results in delayed crop cultivation. Moreover, it’s challenging to predict the environmental conditions and detect the particular area where there are weeds, insects, etc., which requires immediate treatments, thus affecting overall crop production. So, there is a need to make it automated, and this can be done by adopting advanced techniques of precision agriculture (PA) or intelligent agriculture. Precision agriculture is one of the fields in which wireless sensor networks (WSNs) are widely adopted, which consists of a large number of sensors placed in the field to monitor and measure the various environmental parameters, such as humidity, temperature, soil moisture, soil PH value, precipitation, water level, etc., for enhancing the productivity, profitability, quantity, and quality of crops. The machine learning techniques can be applied to precision agriculture to increase crop growth, manage the process of crop cultivation, and create a perfect environment for the crops to increase productivity with less human effort. This paper provides an insight into various machine learning techniques used for precision agriculture using wireless sensor networks.
TL;DR: A transfer learning model called VGG16 (Visual Geometry Group) has been implemented with a focus on breast cancer classification using mammography images taken from the MIAS dataset and has successfully implemented and produced Network’s test score of 87.999 percent.
Abstract: Breast cancer represents the highest percentage of cancers and the second most common cancer overall that affect women with 87,090 deaths approximately as reported by ICMR, 2018 in India (1). Breast tumours are classified into two types as a) benign, which is not very harmful and would not cause breast cancer and b) malignant, the tumours are extremely dangerous and would form an abnormal cell that may cause cancer. One transfer learning model called VGG16 (Visual Geometry Group (16) has been implemented with a focus on breast cancer classification using mammography images taken from the MIAS dataset. Moreover, at the preprocessing step, all the breast images are enhanced by using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique that specifically maintains adaptive techniques to remove the lines, letters, and other boxes irrelevant to the Breast Image. As the name suggests, this VGG16 has taken 13 convolutional layers and 3 fully connected layers to train the dataset. In the training period, this model could predict whether the breast images contain any type of cancerous cells or not. And finally, the model has successfully implemented and produced Network’s test score of 87.999 percent.
TL;DR: The objective of this paper is to explore the research application areas and the widely used approaches/techniques in the domain of machine learning and deep learning.
Abstract: Nowadays, sheer amount of data is being generated by plethora of sources, like science, business, medicine, sports, geography, environment, etc. This produced data may be formless, bigger sized, and in the raw format and has no importance at all, until analyzed. Conventional techniques of data analysis may be inappropriate due to vast data diverse nature, high dimensionality of data, and much of the data is never explored. So, in order to get relevant data, some techniques need to be incorporated on the existing data, which would be effective for the real-world applications. Artificial intelligence, machine learning, and deep learning are the extensively used technologies with the utmost buzz. Machine learning is a subfield of AI that designs the intelligent model based on past and current trends. The only concentration of this ground is pre-programmed learning techniques without any human interference/intervention. In addition to this, deep learning processes the data and creates pattern for decision use after imitating the working of human brain. So, the objective of this paper is to explore the research application areas and the widely used approaches/techniques in the domain of machine learning and deep learning.
TL;DR: Bridging this gap can be done with deep learning feature extraction algorithm and the canny edge detection technique and accuracy closer to the manual results of a human evaluator can be achieved to a significant extent as part of the goal of sustainable development through innovation.
Abstract: To check out the health of the patient, digital images are generated every single day and are used by the radiologist for extracting out the details and anomalies. The complicated part is to figure out the disease in those images. By the manual diagnosis of the images through the radiologists, the doctors can get to know exact scenario of the abnormalities in images, but is considerably more difficult with Content Based Image Retrieval (CBIR) to get those finer details from MR images. These CBIR approaches are now frequently employed in the automatic diagnosis of disease from MR images, mammograms, and other sources. Bridging this gap can be done with deep learning feature extraction algorithm and the canny edge detection technique we propose, and accuracy closer to the manual results of a human evaluator can be achieved to a significant extent as part of the goal of sustainable development through innovation.
TL;DR: In this paper , an epoxy resin matrix for compressive qualities was tested experimentally with the inclusion of fine granite powder and tamarind shell powder particles, and experimental results showed that compressive characteristics of the hybrid bio-composites are greatly improved.
Abstract: Nowadays, hybrid bio-composites are being developed by combining different natural resources as reinforcement and filler components, and this has raised their necessary qualities dramatically. An epoxy resin matrix for compressive qualities was tested experimentally with the inclusion of fine granite powder and tamarind shell powder particles. As reinforcement materials, fine granite powder and tamarind shell powder are employed. Specimens of hybrid bio-composite were created by altering the reinforcement material weight % while maintaining the epoxy resin weight percentage the same. Utilizing a compression moulding process, composite boards made of hybrid biomaterials were created. Water jet machining is used to remove hybrid bio-composite specimens for compression tests in accordance with ASTM standards from the hybrid bio-composite boards. When fine granite and tamarind shell powder particles are added to the epoxy resin matrix, experimental results show that compressive characteristics of the hybrid bio-composites are greatly improved.
TL;DR: This review focuses on the usage of several India-based plants for green production of zinc oxide nanostructures with diverse morphologies and physicochemical properties, and many parameters affecting the morphologies of plant-mediated ZnONS and their benefits over traditional approaches.
Abstract: Zinc oxide nanostructures of variable morphology are extensively used in diversified applications of biomedical sector like antibacterial, anticancer, antifungal and targeted drug delivery utilizations. However, their biocompatibility and cytotoxicity are major issues restricting their commercial development for biomedical applications. Green chemistry is a game touting strategy to fabricate biocompatible zinc oxide nanoparticles for diversified biomedical applications. It eliminates the various bottlenecks associated with physical (high power consumption) and chemical (environmental contamination) fabrication strategies utilized for nanomaterial synthesis.Green strategies majorly use biomass and related materials such as plant extract, essential oils or bacteria, as bio reducing, capping and stabilizing agents for facile fabrication of metal based nanoparticles. It turns these techniques into economic, simpler, eco friendly, non-toxic and less contaminating strategies with high scope of scalable optimization and production. Indian herbs and plants like Mangifera indica, Coriander sativum, Aloe vera are being traditionally used as medicinal remedies and possess high antimicrobial efficacies. The extracts from different parts of these plants like leaves, roots, stem or flower (essential oil) are proven for their bio reducing and stabilizing capabilities in nanomaterial fabrication. This review, for the first time, focuses on the usage of several India-based plants for green production of zinc oxide nanostructures with diverse morphologies and physicochemical properties. The present study covers many parameters affecting the morphologies of plant-mediated ZnO NSs and their benefits over traditional approaches. Besides associated challenges, alternate solutions and prospects are able to attain the United Nations sustainable development goal for environmental sustainability, good health and well being.
TL;DR: A facile hydrothermal process to fabricate activated carbon (AC) from waste jute fiber was reported in this paper , where sequential chemical and thermal activation have been introduced to form a highly functionalized and porous structure.
Abstract: In this work, we reported a facile hydrothermal process to fabricate activated carbon (AC) from waste jute fibre. Sequential chemical and thermal activation have been introduced to form a highly functionalized and porous structure. The prepared AC was characterized by Field Emission Scanning Electron Microscopy (FESEM), X-ray Powder Diffraction (XRD), and Raman Spectroscopy. A hierarchical porous 3D cage-like microstructure was observed from FESEM. Non-linear highly disordered structure and low crystallinity were confirmed by XRD and Raman Spectroscopy. The synthesized AC was then used for batch adsorption study for the removal of an industrial toxic dye, basic blue 41 (BB41). The dependence of the adsorption process on different factors e.g., pH, contact time, and initial concentration has been investigated to find the optimum process conditions. At pH 8, maximum removal of 90 % was achieved. The adsorption was rapid for the first 30 minutes and reaches equilibrium at 70 minutes. Adsorption kinetics were interpreted by pseudo 1st and 2nd order kinetics, and it was best demonstrated by the later one. Langmuir and Freundlich isotherm models were linearized with experimental data; maximum adsorption capacity of 161.30 mg/g was calculated from the Langmuir model, which depicts the actual adsorption capacity.
TL;DR: In this article , two sets of inverted organic solar cells have been constructed by the chemically synthesized ZnO-WO3 nanoparticles as electron transport layer (ETL) and as hole transport layer(HTL).
Abstract: In this study, ZnO-WO3 nanoparticles are prepared by sol-gel technique. Various characterization techniques have been utilized for studying morphological, optical, as well as structural properties of the synthesized nanostructures. Two sets of inverted organic solar cells have been constructed by the chemically synthesized ZnO-WO3 nanoparticles as electron transport layer (ETL) and as hole transport layer (HTL) and the solar cell characteristics were examined. The first solar cell (device A) employing ZnO-WO3 as ETL was fabricated with configuration ITO/ZnO/ZnO-WO3/PTB7-Th:PC71BM/MoO3/Ag, while the other solar cell (device B) employing ZnO-WO3 as HTL was fabricated with structure ITO/ZnO/PTB7-Th:PC71BM/ZnO-WO3/MoO3/Ag. In comparison to the device A with ZnO-WO3 as ETL, a remarkable improvement is seen in device B with ZnO-WO3 as HTL. An efficiency of 1.88% is observed in device B in comparison to the efficiency of 0.64% in device A. The results suggest that ZnO-WO3 nanoparticles have great potential in photovoltaics.
TL;DR: In this paper , the authors discuss the possible applications of social robots in education, the technological and pedagogical challenges they possess, and the ways in which they may affect learning outcomes, concentrating only on robots intended to assist students in learning via social interaction.
Abstract: The article discusses the possible applications of social robots in education, the technological and pedagogical challenges they possess, and the ways in which they may affect learning outcomes, concentrating only on robots intended to assist students in learning via social interaction. Author has highlighted three significant research problems : 1) Does robot tutors help improve students’ learning outcomes? 2) Does appearance and behaviour of robots have a significant role to play on academic engagement of learners? 3) What could be the potential roles of a robot in an educational setting? A statistical meta-analysis of previously published research articles is used to substantiate author’s claims. The larger aim of this article is to provide effective groundwork for future research by describing the expected outcomes of using social robots to offer education and identifying potential research areas for further inquiry.
TL;DR: Insight is provided into the machine learning algorithms widely used by the automotive industries, and a comparison is made between them concerning the Vehicular Ad Hoc Network (VANET) applications.
Abstract: The automotive industry has gained popularity in the past decade, leading to tremendous advancements in intelligent vehicular networks. The increase in the number of vehicles on the roads makes it essential for vehicles to act intelligently as humans do. The concept of machine learning is that when vehicles learn and improve to operate by the previously processed data. The machine learning techniques have helped the automotive industry develop the driverless car. With the help of sensors and cameras, it is quite possible to use the machine learning algorithms and provide the user with its benefits. It helps to allow the vehicle to perform specific tasks that actually can replace the vehicle's driver. The Artificial Intelligence (AI) chips integrated into the vehicles enable the vehicle to navigate roads. This paper provides insight into the machine learning algorithms widely used by the automotive industries, and a comparison is made between them concerning the Vehicular Ad Hoc Network (VANET) applications.
TL;DR: Gaussian Mixture Model provides better accuracy compared to SVM classifiers in segmentation and detection of diabetic retinopathy in retinal images.
Abstract: The main objective of this work is to detect blood vessel segmentation of diabetic retinopathy in low-resolution retinal images using two machine learning algorithms and perform accuracy comparison. Materials and methods: Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) are implemented to detect and segment the blood vessel in retinal images dataset with 40 samples (20 per group). Results: From the MATLAB simulation result, GMM classified the image with better accuracy of 95% compared to SVM classifier accuracy 89%, attained a significant accuracy ratio (p=0.020) in statistical analysis. Conclusion: GMM provides better accuracy compared to SVM classifiers in segmentation and detection.
TL;DR: This paper portrays the various application areas of machine learning, along with its advantages and drawbacks, and describes the various supervised and unsupervised algorithms with their categories, which are used to solve various problems.
Abstract: With the enlightenment of knowledge and the inception of databases in large number, how to fetch the important information from the raw data is the major problem which is needed to be solved. Machine learning is a technique which is helpful in solving this issue with more accuracy and in a faster way. Machine learning working process is to train the data and the algorithm develops some rules, based on that learning, evaluation is done with the test data to generate results without human intervention. This paper portrays the various application areas of machine learning, along with its advantages and drawbacks. This paper also describes the various supervised and unsupervised algorithms with their categories, which are used to solve various problems. This paper will help the researcher in finding various application areas of machine learning and also help the researcher in selection of particular techniques according to the situation. Moreover, the researcher can also learn the detailed knowledge about machine learning. This work can be extended further with comparison of machine learning and deep learning techniques. This area has vast scope and is helpful for the researcher in detection of various crop problems, and disease detection like cancer detection, skin problems, etc.
TL;DR: This study proposes a comprehensive yet brief review of the challenges and opportunities in healthcare sector by using AI and IoT and provides an overview ofAI and IoT, its applicability, some insights about current trends, outlook on future developments, and challenges of healthcare systems.
Abstract: The potential of Artificial Intelligence (AI) enabled Internet of Things(IoT) healthcare is expanded upon to theorize how IoT and AI can improve the accessibility of preventative public health services and transition our current secondary and tertiary healthcare to be a more proactive, continuous, and coordinated system. Also IoT and AI helps in getting obligation and fulfillment by engaging patients to contribute greater imperativeness collaborating with their specialists. Apart from this, IoT has many advantages of streamlining and enhancing healthcare delivery to efficiently predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. This study proposes a comprehensive yet brief review of the challenges and opportunities in healthcare sector by using AI and IoT. It also provides an overview of AI and IoT, its applicability, some insights about current trends, outlook on future developments, and challenges of healthcare systems.
TL;DR: In this paper , the opportunities and advantages of using new technologies to convert agro-waste into biochar based functional materials with their applications in electrochemical supercapacitor were discussed.
Abstract: Burning agricultural waste has become a serious environmental concern, creating health problems and contributing to global warming. The Indian government has sought to address this issue through a variety of policies and initiatives aimed at promoting sustainable management practices, such as transforming agro-waste into electricity. Biochar is a carbon-rich substance made from biomass that has been pyrolysied in low-oxygen environment. This article addresses the opportunities and advantages of using new technologies to convert agro-waste into biochar based functional materials with their applications in electrochemical supercapacitor. We have established an electrode on graphite sheet using biochar as an active material and investigated its electrochemical behavior using cyclic voltammetry and galvanostatic charge-discharge. This strategy will help to overcome the challenges of safe trash recycling as well as the usage of fossil fuels.
TL;DR: It is found that Logistic Regression appears to be better than the SVM, KNN, Decision Tree, and Random Forest in breast cancer detection using the Wisconsin dataset.
Abstract: Machine learning using data mining techniques are used rapidly in medical research to predict the disease diagnosis. The aim of this study is to evaluate the performance of SVM, KNN, Logistic Regression, Random Forest, and Decision Tree. Materials and Methods: A Total of 569 samples are collected from the UCI Machine Learning Laboratory. The samples are divided into benign and malignant cells using groups like SVM, KNN, Decision Tree, Random Forest, and Logistic Regression to compare the performance of benign and malignant cells. The required samples for this analysis are done by G power calculation. Minimum power of analysis is fixed as 0.8 and maximum accepted error is fixed as 0.5. Results: Logistic Regression prediction appears to be better accuracy of 95% than SVM, KNN, Decision Tree, and Random Forest of 92%, 90%, 85%, 91%. Significance of this proposed system is likely to be 0.55. Conclusion: In this study, it is found that Logistic Regression appears to be better than the SVM, KNN, Decision Tree, and Random Forest in breast cancer detection using the Wisconsin dataset.
TL;DR: The various types and classifications of chatbot, their design, and development should bring into the authors' consideration while studying about the chatbot and it is the objective of the review paper.
Abstract: A chatbot is computerized application program, which is using to interact with human (like a human conversation) known as a chatbot. Artificial intelligence or various techniques of artificial intelligence works as a backbone behind the smartness of the chatbot. With more artificial intelligence, machine-learning, and deep-learning development, some it gives the feelings like either talking to a human or chatbot. This review article offers potential knowledge on the basic concepts of chatbot. Future designers may understand chatbot with precisely and get the ability to use and build them properly for the function they want to run. However, with the approaching of new technology intelligent chatbot systems are developing using "knowledge-based models." The various types and classifications of chatbot, their design, and development. It should bring into our consideration while studying about the chatbot and it is the objective of the review paper.
TL;DR: In this paper , a plant-based synthesis from the medicinal plant Indigofera tinctora (L.) (IBLH) was used for detecting heavy metal ions.
Abstract: Plant-based synthesis of nanomaterials is a more reliable method since it is easy, quick, and environmentally friendly, and it does not require any specific conditions, unlike other methods. For the first time, we report the sensing of metal ions using a fluorescent nano-carbon material via a plant-based synthesis from the medicinal plant, Indigofera tinctora (L.) (IBLH). This nanomaterial from the leaf extract of IBLH was synthesized by hydrothermal assisted green synthesis method. The as-synthesized sample was characterized by various spectroscopic techniques for confirming the formation of nano-carbon material. Optical studies revealed that IBLH was influential in determining toxic heavy metal ions (Pb2+). Detection of Pb2+ was observed from a range of 1M to as low as 1nM using IBLH as the probe. Stern-Volmer plot exhibits the progressive detection of the metal ion, proving that the IBLH nano-carbon material is capable of progressive sensing of various heavy metal ions.