Browsing by Author "Singh, S."

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A comparative analysis of crustacean exoskeletons: structural, microstructural, morphological, and UV absorption studies
(Institute of Physics, 2024) Nowl, M.S.; Praveen, L.L.; Ambili, V.; Singh, S.; Samad, U.; Seikh, A.H.; Dutta, S.; Mandal, S.
This study aims to investigate the structural, thermal, and spectral characteristics, along with the ultra-violet (UV) absorption of various marine benthos exoskeletons, such as various species of crabs (Portunus sanguinolentus, Portunus pelagicus, Charybdis feriata) and mantis shrimp (Oratosquilla oratoria). Their unique properties and ability to survive in harsh oceanic environments make them interesting research subjects. This research utilized powder x-ray diffraction (XRD) analysis to determine the crystal structure of the benthic varieties. The sample surface was analyzed using high-resolution micrographs obtained from field-emission scanning electron microscopy (FESEM), which identified the presence of chitin and calcite in the marine benthos. This was further confirmed by differential scanning calorimetry (DSC), and Fourier transform infrared spectroscopy (FTIR). The optical characteristics were investigated using UV-visible spectroscopy. The proximate analysis revealed high protein content in the mantis shrimp exoskeleton compared to other crab species, highlighting its excellent UV absorption characteristics. Overall, this research has the potential to broaden our understanding of marine organisms, which can have potential applications in biotechnology and materials science to develop nature-inspired innovative materials sustainably. © 2024 The Author(s). Published by IOP Publishing Ltd.
A Systematic Mapping Study of Content Based Filtering Recommender Systems
(Springer Science and Business Media Deutschland GmbH, 2019) Jain, M.; Singh, S.; Chandrasekaran, K.
There has been extremely limited use of recommender systems for clothing suggestions. A clear idea of where recommender systems are used would facilitate the correct method of implementation for the domain given above. In order to propose a solution, there is a need to properly analyse the various existing approaches and solutions developed in a particular field. This study will help us gain clarity to answer several research questions in the chosen domain. A systematic mapping study is carried out to identify as well as classify the research papers pertaining to the chosen field. © 2019, Springer Nature Switzerland AG.
An overview of the shared task on machine translation in Indian languages (MTIL)-2017
(De Gruyter peter.golla@degruyter.com, 2019) Anand Kumar, M.A.; Premjith, B.; Singh, S.; Rajendran, S.; Padannayil, K.P.
In recent years, the multilingual content over the internet has grown exponentially together with the evolution of the internet. The usage of multilingual content is excluded from the regional language users because of the language barrier. So, machine translation between languages is the only possible solution to make these contents available for regional language users. Machine translation is the process of translating a text from one language to another. The machine translation system has been investigated well already in English and other European languages. However, it is still a nascent stage for Indian languages. This paper presents an overview of the Machine Translation in Indian Languages shared task conducted on September 7-8, 2017, at Amrita Vishwa Vidyapeetham, Coimbatore, India. This machine translation shared task in Indian languages is mainly focused on the development of English-Tamil, English-Hindi, English-Malayalam and English-Punjabi language pairs. This shared task aims at the following objectives: (a) to examine the state-of-the-art machine translation systems when translating from English to Indian languages; (b) to investigate the challenges faced in translating between English to Indian languages; (c) to create an open-source parallel corpus for Indian languages, which is lacking. Evaluating machine translation output is another challenging task especially for Indian languages. In this shared task, we have evaluated the participant's outputs with the help of human annotators. As far as we know, this is the first shared task which depends completely on the human evaluation. © 2019 Walter de Gruyter GmbH, Berlin/Boston.
Deterministic seismic hazard and landslide hazard zonation of Arunachal Pradesh
(Springer, 2022) Anand, G.; Rahangdale, A.; Mantri, S.S.; Singh, S.; Kolathayar, S.
This paper presents a seismically induced landslide hazard assessment for the state of Arunachal Pradesh, India, based on GIS techniques. A comprehensive earthquake catalog was prepared with data from various sources like USGS, ISC, etc., within a rectangular enclosure having a distance of 500 km in four cardinal directions from the Arunachal Pradesh state boundary. The catalog was homogenized in a unified moment magnitude scale. The earthquake data were collected for a period ranging from the 1500s to the year 2020. The earthquakes having a magnitude ≥4 are considered for this study as they are mainly responsible for inducing enough horizontal movement along the slopes for landslides. Considering the linear source model, the deterministic seismic hazard analysis was performed to estimate peak horizontal acceleration (PHA) at the bedrock level. The log-likelihood method was employed to decide the most efficient and reliable ground motion prediction equation (GMPE) for the Arunachal Pradesh region. Then peak ground acceleration (PGA) values generated at the surface due to the shaking of bedrock were calculated using a non-linear site amplification (considering the soil nature as B-type NHERP classification). The PGA values were considered to induce driving force on slopes, thus causing a landslide. The topographical slope map of Arunachal Pradesh was developed from CARTOSAT Digital Elevation Model Data (30m resolution). The study region was divided into 50 × 50 m2 grids. The seismically induced landslide hazard assessment was performed using Newmark’s methodology using PGA values and slope angles at the center of each grid. The critical factor of safety necessary to counter the landslide for corresponding PGA values was determined, and its spatial variation in the state is presented as contour maps. For any grid point in the study region, if the in-situ (available) static factor of safety is higher than the static factor of safety necessary to counter the landslide as predicted in the current study, that slope is regarded to be safe. © 2022, Indian Academy of Sciences.
Diabetic Retinopathy Detection Using Novel Loss Function inÂ Deep Learning
(Springer Science and Business Media Deutschland GmbH, 2024) Singh, S.; Annappa, B.; Dodia, S.
Globally, the number of diabetics has significantly increased in recent years. Several age groups are affected. Diabetic Retinopathy (DR) affects those with diabetes for a long time. DR is a side effect of diabetes that affects the retinaâ€™s blood vessels and is caused by high blood sugar levels. Therefore, early detection and treatment are preferred. Manual recognition concerns and a lack of technology support for ophthalmologists are the most complex problems. Nowadays, Deep Learning (DL) based approaches are used significantly for creating DR detection systems because of the ongoing development of Artificial Intelligence (AI) techniques. This paper uses the APTOS dataset of retina images to train four deep Convolution Neural Network (CNN) models using a novel loss function. The four DL models used are VGG16, Resnet50, DenseNet121, and DenseNet169 to explain their rich properties and improve the classification for different phases of DR. The experimental results of this study demonstrate that VGG16 produced the lowest accuracy of 73.26% on the APTOS dataset, while DenseNet169-based detection gives the most significant result of 96.68% accuracy among the four approaches. Â© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Drilling parameter optimization of cenosphere/HDPE syntactic foam using CO2 laser
(Elsevier Ltd, 2022) Singh, S.; Yaragatti, N.; Doddamani, M.; Powar, S.; Zafar, S.
High-density polyethylene is a high-strength, and low-weight material system. Besides numerous applications in a variety of fields and products, its machining for generation of holes is rather difficult with traditional methods such as drilling as the process is not very conducive for composites due to associated damage. Hence, a non-contact material removal process such as laser machining provides an appealing, cost-effective, accurate, and fast alternative. For this study, the effect of the laser process controls key parameters such as laser power and laser speed on the cut surface integrity defined by surface roughness, kerf taper angle, and heat-affected zone of neat HDPE and HDPE with 60 wt% cenosphere was investigated and optimized using response surface methodology. Also, the machining operation was visualized using a Photron FASTCAM SA 1.1 high-speed camera to observe the effects of the high-intensity laser beam on specimens and to investigate the mechanism of laser machining. The optimum values for a defect-free cut surface (minimum surface roughness and low kerf taper angle) in neat HDPE comes out to be as laser power of 97.5 W and laser speed of 5 mm/s, with corresponding surface roughness and kerf taper angle of 54.304 μm and 0.152 degrees respectively and the optimum input values for HDPE with 60 wt% cenosphere are 102.126 W laser power and 5 mm/s laser speed, with corresponding surface roughness and kerf taper angle of 26.574 μm and 0.253 degrees. This study finds importance for the industrial and medical application to creates small size holes for mechanical joints such as rivets, bolts, and screws in assembly as low surface roughness and kerf width are always preferred as quality parameters in creating holes for industrial applications. © 2022
Durability of Bricks Coated with Red mud Based Geopolymer Paste
(2016) Singh, S.; Basavanagowda, S.N.; Aswath, M.U.; Ranganath, R.V.
The present study is undertaken to assess the durability of concrete blocks coated with red mud - fly ash based geopolymer paste. Concrete blocks of size 200 x 200 x 100mm were coated with geopolymer paste synthesized by varying the percentages of red mud and fly ash. Uncoated concrete blocks were also tested for the durability for comparison. In thermal resistance test, the blocks were subjected to 600�C for an hour whereas in acid resistance test, they were kept in 5% sulphuric acid solution for 4 weeks. The specimens were thereafter studied for surface degradation, strength loss and weight loss. Pastes with red mud percentage greater than 50% developed lot of shrinkage cracks. The blocks coated with 30% and 50% red mud paste showed better durability than the other blocks. The use of blocks coated with red mud - fly ash geopolymer paste improves the aesthetics, eliminates the use of plaster and improves the durability of the structure. � Published under licence by IOP Publishing Ltd.
Durability of Bricks Coated with Red mud Based Geopolymer Paste
(Institute of Physics Publishing michael.roberts@iop.org, 2016) Singh, S.; Basavanagowda, S.N.; Aswath, M.U.; Ranganath, R.V.
The present study is undertaken to assess the durability of concrete blocks coated with red mud - fly ash based geopolymer paste. Concrete blocks of size 200 x 200 x 100mm were coated with geopolymer paste synthesized by varying the percentages of red mud and fly ash. Uncoated concrete blocks were also tested for the durability for comparison. In thermal resistance test, the blocks were subjected to 600Â°C for an hour whereas in acid resistance test, they were kept in 5% sulphuric acid solution for 4 weeks. The specimens were thereafter studied for surface degradation, strength loss and weight loss. Pastes with red mud percentage greater than 50% developed lot of shrinkage cracks. The blocks coated with 30% and 50% red mud paste showed better durability than the other blocks. The use of blocks coated with red mud - fly ash geopolymer paste improves the aesthetics, eliminates the use of plaster and improves the durability of the structure. Â© Published under licence by IOP Publishing Ltd.
Entropy engineering in I-V-VI2 family: a paradigm to bestow enhanced average ZT in the entire operating temperature regime
(Royal Society of Chemistry, 2024) Basu, R.; Shenoy, U.S.; Pathak, A.; Singh, S.; Jha, P.; Bhat, D.K.; Basu, H.; Singh, A.
The design and development of n-type alloys in the mid-temperature regime (500-700) K with enhanced thermoelectric performance is of utmost necessity for the fabrication of thermoelectric devices. In this regard, the I-V-VI2 family reveals superior thermoelectric performance, owing to the fact that group V elements have non-bonded electrons and high Z (atomic number), with a high Grüneisen parameter, which cause amplified anharmonicity and subsequently low intrinsic lattice thermal conductivity. However, the irony is that the well-studied alloy of this family, AgBiSe2, undergoes phase transition in the operating temperature range. Thus, of paramount importance is restricting the phase transition and bringing it down below room temperature (RT), along with stabilizing a highly symmetrical crystal structure in the extended operating temperature range. Efforts were made to synthesize a cubic n-type AgBiSeS alloy belonging to the I-V-VI2 compounds (unlike AgBiSe2) that is stabilized throughout the temperature range, as the S element aids in strengthening of the chemical bonds. In addition, the alloy was further stabilized by forming a solid solution with PbSe, which aids in increasing the configurational entropy and thereby increases the chemical space of the system. The resultant alloys possess intrinsically low lattice thermal conductivity ranging from 0.38-0.74 W m−1 K−1 in the entire operating range. Consequently, the peak ZT was reported as ∼0.6 at 780 K, with an average ZT value of 0.3 for the alloy (AgBiSeS)0.5(PbSe)0.5 within 300-823 K. Although the reported ZT is low, the methodology of entropy-driven structural stabilization in the operating temperature regime was adapted to attain a highly symmetrical, stable structure for practical applications. © 2024 RSC.
High figure-of-merit in Zn, Sb co-doped Mg2Si0.3Sn0.7 alloy through simultaneous optimization of electrical and thermal transports
(Elsevier Ltd, 2025) Sarkar, P.; Gupta, P.; Shenoy, U.S.; Singh, S.; Kundu, S.; Kumawat, N.; Kedia, D.K.; Bhat, D.K.; Bhattacharya, S.; Singh, A.
The derivatives of Mg2Si have recently attracted wide attention as promising thermoelectric materials due to earth abundant and environment friendly low-cost constituents. The main challenge in optimizing the thermoelectric figure of merit ZT, is the low electrical and high thermal conductivities of Mg2Si. The present study demonstrates high ZT of ?1.55 at 673 K in Mg2Si0.3Sn0.7 through simultaneous optimization of electrical and thermal transport through Sb and Zn co-doping. The ultra-low deformation and alloy scattering potentials in Sb and Zn co-doped samples helps in maintaining record high Hall mobility ?70–90 cm2/V.s. The doping induced pudding mold band structure with hyperconvergence in conduction band balances high Seebeck coefficient and high electrical conductivity. The point defects and dislocations created by doping helps in lowering of lattice thermal conductivity as well. The uni-leg power generator fabricated using optimized Mg1.96Zn0.04(Si0.3Sn0.7)0.98Sb0.02 exhibits a record efficiency of ?9.5 % at ?T ? 329 K. © 2025
Impact of Building Configurations on Fluid Flow in an Urban Street Canyon
(Springer Science and Business Media Deutschland GmbH, 2024) Singh, S.; Singh, L.; Jitendra Pal, S.
The problem of pollution dispersion in urban areas is significant in the densely populated cities. The topography and barriers in the form of buildings impact the atmospheric fluid flow. The resulting phenomena known as pollution traps cause an artificial dispersion in the buildingsâ€™ proximity, affecting the health of ordinary road commuters. The primary source of pollution on the street canyons is exhaust gases from the vehicle movements. However, the concern is associated with the poor dispersion of pollutants under normal wind conditions. The primary reason behind the poor dispersion is the buildings that act as obstacles to the atmospheric wind flow. Thereby it is essential to comprehend the behaviour of pollutants under given shape constraints and flow conditions to improve urban air quality. The present study investigates the wind flow in the proximity of a six-storey building for a medium street canyon configuration under the logarithm inlet velocity profile that acts as atmospheric boundary layer (ABL). Effect of important parameters such as the building height, the wind direction (0, 30, 45, 60, and 90Â°), and building configurations (straight road, both side building, and only upwind side building with downwind side building) are investigated to gain valuable insights into pollutant dispersion. The analysis of turbulence and velocity profile in the domain at nose level (1.5 m above ground level) leeward sidewalk and windward sidewalk shows turbulent intensity decreases at the nose (breathing) level with building height; however, it increases when the approach angle is 450 suggesting the formation of dominant pockets of pollutants. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Linux-like Socket Statistics Utility for ns-3
(Association for Computing Machinery, 2023) Rudra, A.R.; Somayaji, S.L.; Singh, S.; Mokashi, S.D.; Rakshit, A.; Khan, D.; Tahiliani, M.
Collecting statistics in network experiments is crucial for understanding the behavior of the network protocols and identifying any anomalies or performance issues. Without accurate and comprehensive statistics, it is difficult to analyze network traffic, identify bottlenecks, and make informed decisions about network protocol improvements. One of the key features of ns-3 is its ability to collect detailed statistics about network behavior during simulations. It supports various modules to collect statistics, such as Flow Monitor to collect flow level statistics, trace sources to collect information about specific events that occur during simulation, packet captures (PCAP) that can be read and analyzed using various PCAP-compatible tools and ASCII traces for debugging and generating custom reports. Besides, ns-3 also provides a flexible and extensible framework for users to create their own custom statistics collection modules. Nevertheless, collecting and analyzing data from simulations using these tools can be a complex process and requires a good understanding of the ns-3 simulation framework and its internal data structures. This paper discusses the design and development of a Linux-like socket statistics (ss) utility for ns-3 which makes the task of gathering network statistics much simpler. The main objective of this work is to develop a user-friendly API that enables ns-3 users to easily generate socket statistics. We validate the proposed API by comparing the results obtained from the trace sources already present in ns-3, and observe a high degree of consistency between our API and the trace source results. In addition, we analyze the impact of the proposed API on ns-3 performance in terms of resource consumption. Â© 2023 ACM.
Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe
(Institute of Electrical and Electronics Engineers Inc., 2020) Jain, M.; Singh, S.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.
In the present-day scenario, several clothing recommender systems have been developed for the online e-commerce industry. However, when it comes to recommending clothes that a person already possesses, i.e, from their personal wardrobe, there are very few systems that have been proposed to perform the task. In this paper, we tackle the latter issue, and perform experimental analysis of the various Machine Learning techniques that can be used for carrying out the task. Since the recommendations must be made from a user's personal wardrobe, the recommender system doesn't follow a traditional approach. This is explained in detail in the following sections. Further, the paper contains a complete description of the results obtained from the experiments conducted, and the best approach is specified, with appropriate justification for the same. Â© 2020 IEEE.
Mango Leaves (Mangifera indica)-Derived Highly Florescent Green Graphene Quantum Dot Nanoprobes for Enhanced On-Off Dual Detection of Cholesterol and Fe2+ Ions Based on Molecular Logic Operation
(American Chemical Society, 2024) Ratnesh, R.K.; Singh, M.K.; Kumar, V.; Singh, S.; Chandra, R.; Singh, M.; Singh, J.
In the present study, we have engineered a molecular logic gate system employing both Fe2+ ions and cholesterol as bioanalytes for innovative detection strategies. We utilized a green-synthesis method employing the mango leaves extract to create fluorescent graphene quantum dots termed “mGQDs”. Through techniques like HR-TEM, i.e., high-resolution transmission electron microscopy, Raman spectroscopy, and XPS, i.e., X-ray photoelectron spectroscopy, the successful formation of mGQDs was confirmed. The photoluminescence (PL) characteristics of mGQDs were investigated for potential applications in metal ion detection, specifically Fe2+ traces in water, by using fluorescence techniques. Under 425 nm excitation, mGQDs exhibited emission bands at 495 and 677 nm in their PL spectrum. Fe2+-induced notable quenching of mGQDs’ PL intensity decreased by 97% with 2.5 μM Fe2+ ions; however, adding 20 mM cholesterol resulted in a 92% recovery. Detection limits were established through a linear Stern-Volmer (S-V) plot at room temperature, yielding values of 4.07 μM for Fe2+ ions and 1.8 mM for cholesterol. Moreover, mGQDs demonstrated biocompatibility, aqueous solubility, and nontoxicity, facilitating the creation of a rapid nonenzymatic cholesterol detection method. Selectivity and detection studies underscored mGQDs’ reliability in cholesterol level monitoring. Additionally, a molecular logic gate system employing Fe2+ metal ions and cholesterol as a bioanalyte was established for detection purposes. Overall, this research introduces an ecofriendly approach to craft mGQDs and highlights their effectiveness in detecting metal ions and cholesterol, suggesting their potential as versatile nanomaterials for diverse analytical and biomedical applications. © 2024 American Chemical Society.
Mitigating Masquerade using Nonce in Symmetric Key Distribution - Survey
(Institute of Electrical and Electronics Engineers Inc., 2020) Pandiya, C.; Singh, S.; Chandavarkar, B.R.
Key distribution deals with mechanisms for secure distribution of keys. Since symmetric key cryptography requires both parties(encryption and decryption) to use the same key, the security of key distribution techniques is pivotal to the secrecy of overall exchange. This process generally uses master keys and session keys for key distribution and can be further strengthened by making use of nonces. A nonce is a non-repeating value (number used once) which may or may not be random. Such a value can be incorporated in cryptographic algorithms so as to make guessing and predicting difficult for an adversary during the exchange of messages between two entities over the network. This can help in mitigating masquerading and replay attacks. A masquerade attack is a kind of active attack wherein one entity pretends to be a different entity. Some other kinds of active attacks such as replay attack and modification of messages can also be grouped under the umbrella of masquerade attack. Such attacks often take advantage of the predictable nature of certain steps during the exchange of messages between two entities over the network. In this paper, we explore the usage of nonces in various cryptographic and network security applications in symmetric key distribution environment so as to prevent active attacks like masquerade attacks. Â© 2020 IEEE.
Multimodal group activity state detection for classroom response system using convolutional neural networks
(Springer Verlag service@springer.de, 2019) Sebastian, A.G.; Singh, S.; Manikanta, P.B.T.; Ashwin, T.S.; Guddeti, R.M.R.
Human–Computer Interaction is a crucial and emerging field in computer science. This is because computers are replacing humans in many jobs to provide services. This has resulted in the computer being needed to interact with the human in the same way as the human does with another. When humans talk to each other, they gain feedback based on how the other person responds non-verbally. Since computers are now interacting with humans, they need to be able to detect these facial cues and accordingly adjust their services based on this feedback. Our proposed method aims at building a Multimodal Group Activity State Detection for Classroom Response System which tries to recognize the learning behavior of a classroom for providing effective feedback and inputs to the teacher. The key challenges dealt here are to detect and analyze as many students as possible for a non-biased evaluation of the mood of the students and classify them into three activity states defined: Active, passive, and inactive. © Springer Nature Singapore Pte Ltd. 2019
Performance Analysis of PV Module Using Pyramid Surface Texturing Approach
(Springer Science and Business Media Deutschland GmbH, 2022) Bansal, R.K.; Singh, S.; Singh, A.K.; Waseem Ahmad, M.
Performance analysis of thin-film solar modules has been done using the pyramid texturing technique. To change the geometry of the surface of the solar cell through surface texturing technique, it increases the effective area of thin-film module. Significant improvement has been found by inserting a random pyramid structure. TCAD and PvSyst software is used to design and development of surface texturing and temperature-dependent loss minimization. Efficiency improvement of 3% has been achieved using this noble approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Rapid sonochemical synthesis of copper doped ZnO grafted on graphene as a multi-component hierarchically structured visible-light-driven photocatalyst
(Elsevier Ltd, 2021) Shenoy, S.; Ahmed, S.; Lo, I.M.C.; Singh, S.; Sridharan, K.
Three-dimensional (3D) hierarchical structures (HSs) have demonstrated excellent properties for various applications that are attributable to their distinctive micro-sized architecture with nanoscale substructures. Recently, the ultrarapid sonochemical approach was found to be an effective strategy for synthesizing single component HSs with uniform morphologies in comparison to the direct precipitation technique. We here report the fabrication of copper doped zinc oxide grafted on graphene layers (ZnO-Cux-GOy) for exploring the capability of this ultrarapid approach for synthesizing multi-component HSs. Interestingly, the morphology of ZnO-Cux-GOy HSs studied through electron microscopy revealed the growth of ZnO HSs decorated with Cu nanoparticles and interconnected by graphene layers. ZnO-Cux-GOy HSs demonstrated three-fold higher efficiency in the photodegradation of ibuprofen (IBU) under visible light irradiation in comparison to pristine ZnO HSs, which is attributable to the combined influence of the doped Cu2+ ions and graphene, enabling improved visible light absorption and inhibiting the recombination of photogenerated charges. Thus, the novel ultrarapid sonochemical synthesis strategy demonstrated here is anticipated to open up a new horizon for the time-saving and scalable design of multi-component HSs of various materials for a myriad of applications. © 2021 Elsevier Ltd
Transfer Learning-Hierarchical Segmentation on COVID CT Scans
(Springer, 2024) Singh, S.; Pais, A.R.; Crasta, L.J.
COVID-19—A pandemic declared by WHO in 2019 has spread worldwide, leading to many infections and deaths. The disease is fatal, and the patient develops symptoms within 14 days of the window. Diagnosis based on CT scans involves rapid and accurate detection of symptoms, and much work has already been done on segmenting infections in CT scans. However, the existing work on infection segmentation must be more efficient to segment the infection area. Therefore, this work proposes an automatic Deep Learning based model using Transfer Learning and Hierarchical techniques to segment COVID-19 infections. The proposed architecture, Transfer Learning with Hierarchical Segmentation Network (TLH-Net), comprises two encoder–decoder architectures connected in series. The encoder–decoder architecture is similar to the U-Net except for the modified 2D convolutional block, attention block and spectral pooling. In TLH-Net, the first part segments the lung contour from the CT scan slices, and the second part generates the infection mask from the lung contour maps. The model trains with the loss function TV_bin, penalizing False-Negative and False-Positive predictions. The model achieves a Dice Coefficient of 98.87% for Lung Segmentation and 86% for Infection Segmentation. The model was also tested with the unseen dataset and has achieved a 56% Dice value. © The Author(s), under exclusive licence to The Japanese Society for Artificial Intelligence and Springer Nature Japan KK, part of Springer Nature 2024.
Trustworthiness of COVID-19 News and Guidelines
(Springer, 2023) Singh, S.; Nagar, L.; Lal, A.; Chandavarkar, B.R.
COVID-19 pandemic is a serious health concern issue over the past couple of years. It spreads mostly due to bio-contacts, which leads people to follow social distancing and stay away from social gatherings. It leads the people to bound themselves to stay with their family members at their home only, being at home, staying idle, or following work from home schedule by working online through the Internet over the electronic gadgets such as mobiles, laptops, desktops, etc. It leads the people to attach to online activities more for spending their time at their home, which enormously increases people interest in social media platforms such as Twitter, Facebook, etc. As it was a major pandemic period, it created panic and a fearful situation in society. It makes the people believe any news and guidelines spreading through social media platforms irrespective of checking their trustworthiness and truthiness of it. This pandemic period created a seriously bad impact on societyâ€™s emotional, physical, and mental health that is a great loss to a country even all over the world. Under this, many unwanted messages are spreading for oneâ€™s interest or a group to polarize their interest. In a panic situation, it is highly required of a solution that prevents the spread of these negative vibes to maintain the overall health of society. This chapter tries to implement an optimal solution using various kinds of layers and different optimization functions. It particularly gives better performance in the case of sequential data using machine learning (ML) and deep learning (DL) frameworks trained with the dataset for identifying the fake news and guidelines spread over on COVID-19. To train the model, a dataset was taken from the Twitter Application Programming Interface (API). Finally, the truthiness detection technique with social interaction is completed using Twitter dataset. The efficacy of the suggested method is demonstrated by the obtained results on a Twitter dataset. Â© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.