Vishwakarma University

Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfills humans’ basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like India. Agriculture contributes 15.4% in the GDP of India. Agriculture activities are broadly categorized into three major areas: pre-harvesting, harvesting and post harvesting. Advancement in area of machine learning has helped improving gains in agriculture. Machine learning is the current technology which is benefiting farmers to minimize the losses in the farming by providing rich recommendations and insights about the crops. This paper presents an extensive survey of latest machine learning application in agriculture to alleviate the problems in the three areas of pre-harvesting, harvesting and post-harvesting. Application of machine learning in agriculture allows more efficient and precise farming with less human manpower with high quality production.

The current research explores the problem of steady laminar flow of nanofluid on a two dimensional boundary layer using heat transfer of Cassona cross the linearly stretching sheet. The governing equations are partial differential equations which are transformed into non-linear ordinary differential equations by using some similarity transformation. The converted form of the combined non-linear higher-order ODEswith a set of boundary conditions are solved by means of Runge-Kutta 4th-order approach along with the shooting method. The nanoparticle concentration profiles, velocity, and temperature are examined by taking account of their influence of Prandtl number, “Brownian motion parameter”, Lewis number, thermophoresis, and Casson fluid parameter. It is reported that the temperature increase as Nt and Nb increases which causes thickening of the thermal boundary layer. Also it is observed that, there is increment in temperature profile for increasing values of Brownian motion parameter and the energy distribution grows with increment in the values of Thermophoresis parameter. The comparison for the local Nusselt & local Sherwood number has been tabulated with respect to variation of the Brownian Motion Parameter and Thermophoresis parameter. All the findings of the results are graphically represented and discussed.

The NavGuide is a novel electronic device to assist visually impaired people with obstacle free path-finding. The highlight of the NavGuide system is that it provides simplified information on the surrounding environment and deduces priority information without causing information overload. The priority information is provided to the user through vibration and audio feedback mechanisms. The proof-of-concept device consists of a low power embedded system with ultrasonic sensors, vibration motors, and a battery. To test the effectiveness of the NavGuide system in daily-life mobility of visually impaired people, we performed an evaluation using 70 blind people of the “school & home for the blind.” All evaluations were performed in controlled, real-world test environments with the NavGuide and traditional white cane. The evaluation results show that NavGuide is a useful aid in the detection of obstacles, wet floors, and ascending staircases and its performance is better than that of a white cane.

Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals.

Ultraviolet (UV) radiation is the primary cause of various skin diseases, necessitating the need for UV protection. Topical sunscreens are the most commonly used method to achieve this. However, traditional sunscreen formulations have limitations that hinder their widespread use. Thanks to advancements in nanotechnology, new nanotechnology-based sunscreens have been developed, addressing these limitations. The review explores different nanosystems utilized in sunscreen formulations, including polymeric nanoparticles, liposomes, nanostructure lipid carriers, solid lipid nanoparticles, nanoemulsions, hydrogels, nanocrystals, mesoporous silica particles, niosomes, ethosomes, transfersomes, transethosomes, and sunspheres. These nanosystems enhance the safety and effectiveness of sunscreens, improving their distribution, photostability, SPF, UVA protection, and water resistance. Combinational sunscreens, which combine multiple active ingredients, are also discussed. They offer broad-spectrum protection against UVA and UVB radiation, providing comprehensive sun protection. The article reviews evaluation methods for sunscreens, such as SPF, UVA protection, and water resistance. SPF measures the level of UVB protection, while UVA protection indicates defense against UVA radiation. Water resistance assesses the sunscreen's durability after exposure to water or sweat. Additionally, the article addresses the safety, regulation, and challenges associated with nanosystem-based sunscreens. Safety considerations and regulatory frameworks ensure the products' safety for human health and the environment. Formulation stability, potential toxicity concerns, and lack of public awareness are also discussed as challenges. In summary, nanotechnology-based sunscreens offer promising advancements in UV protection. The utilization of various nanosystems improves safety and efficacy. Ongoing research and regulatory efforts are vital to ensure the continued development and safe use of these nanosystems in sunscreens.

Healthcare big data (HBD) allows medical stakeholders to analyze, access, retrieve personal and electronic health records (EHR) of patients. Mostly, the records are stored on healthcare cloud and application (HCA) servers, and thus, are subjected to end-user latency, extensive computations, single-point failures, and security and privacy risks. A joint solution is required to address the issues of responsive analytics, coupled with high data ingestion in HBD and secure EHR access. Motivated from the research gaps, the paper proposes a scheme, that integrates blockchain (BC)-based confidentiality-privacy (CP) preserving scheme, CP-BDHCA, that operates in two phases. In the first phase, elliptic curve cryptographic (ECC)-based digital signature framework, HCA-ECC is proposed to establish a session key for secure communication among different healthcare entities. Then, in the second phase, a two-step authentication framework is proposed that integrates Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES), named as HCA-RSAE that safeguards the ecosystem against possible attack vectors. CP-BDAHCA is compared against existing HCA cloud applications in terms of parameters like response time, average delay, transaction and signing costs, signing and verifying of mined blocks, and resistance to DoS and DDoS attacks. We consider 10 BC nodes and create a real-world customized dataset to be used with SEER dataset. The dataset has 30,000 patient profiles, with 1000 clinical accounts. Based on the combined dataset the proposed scheme outperforms traditional schemes like AI4SAFE, TEE, Secret, and IIoTEED, with a lower response time. For example, the scheme has a very less response time of 300 ms in DDoS. The average signing cost of mined BC transactions is 3,34 seconds, and for 205 transactions, has a signing delay of 1405 ms, with improved accuracy of ≈ 12% than conventional state-of-the-art approaches.

To provide autonomous navigation and orientation to visually impaired people, this article proposes a new electronic assistive device called the NavCane. The device helps people find obstacle-free paths in both indoor and outdoor settings. The NavCane also aids in the recognition of objects in an indoor setting. The advantage of the NavCane device is that it provides priority information about obstacles in the path without causing information overload. The priority information deduced by the system is transmitted to the user using tactile and auditory communication methods. Unlike existing electronic travel assistance systems which are limited to obstacle detection and path finding, the NavCane also helps users by recognizing objects in known indoor settings. The developed prototype is low cost and as a low power embedded device for obstacle detection and obstacle identification, it is an alternative to machine vision systems. It has a radio-frequency identification reader, ultrasonic sensors, a global system for mobile communication module, a global positioning system module, vibration motors, a gyroscope, a wet floor sensor, and a battery. To test the usefulness of the NavCane in mundane commuting, object recognition, and rehabilitation for visually impaired people, we assessed it with the help of 80 visually impaired people from a blind school and a home for elderly people. All the assessments were executed in controlled indoor and outdoor test environments with both a NavCane and a white cane. The experimental results show that the NavCane is an effective device for detecting of obstacles, ascending and descending staircases, navigating wet floors, and object recognition in environments that are known and unknown to the user. In addition, our evaluation results indicate that the NavCane improves the performance of obstacle-free navigation compared to a white cane.

Temperature-induced, rapid changes in the viscosity and reproducible 3-D structure formation makes thermos-sensitive hydrogels an ideal delivery system to act as a cell scaffold or a drug reservoir. Moreover, the hydrogels' minimum invasiveness, high biocompatibility, and facile elimination from the body have gathered a lot of attention from researchers. This review article attempts to present a complete picture of the exhaustive arena, including the synthesis, mechanism, and biomedical applications of thermosensitive hydrogels. A special section on intellectual property and marketed products tries to shed some light on the commercial potential of thermosensitive hydrogels.

The analysis of heat dissipation over a layered stretching sheet under the control of magneto-hydrodynamic mixed convective flow of Eyring-Powell fluid is described in this study. The effect of heat emission and immersion is investigated. A viscous, incompressible, two-dimensional, and laminar fluid is assumed. The governing equations of momentum and temperature pictures are translated into a collection of non-linear differential equations using conformable similarity transformations. To obtain the mathematical solution of the governing equations, the shooting approach is modified. Fourth-order Runge-Kutta formulation is applied for the integration and Newton's formulation suffices to simplify initial guess values. MATLAB is used for all the programming. The effect of respective distinct flow parameters on the temperature, velocity, represented through graphical forms, and the interpretation of some helpful engineering aggregates such as the skin-friction coefficient and Nusselt number are explained graphically for different variables. It has been shown that increasing the thermal stratification parameter reduces fluid velocity and also temperature, and vice versa is noticed for the heat production variable.

Deep learning-based machine learning models have shown significant results in speech recognition and numerous vision-related tasks. The performance of the present speech-to-text model relies upon the hyperparameters used in this research work. In this research work, it is shown that convolutional neural networks (CNNs) can model raw and tonal speech signals. Their performance is on par with existing recognition systems. This study extends the role of the CNN-based approach to robust and uncommon speech signals (tonal) using its own designed database for target research. The main objective of this research work was to develop a speech-to-text recognition system to recognize the tonal speech signals of Gurbani hymns using a CNN. Further, the CNN model, with six layers of 2DConv, 2DMax Pooling, and 256 dense layer units (Google’s TensorFlow service) was also used in this work, as well as Praat for speech segmentation. Feature extraction was enforced using the MFCC feature extraction technique, which extracts standard speech features and features of background music as well. Our study reveals that the CNN-based method for identifying tonal speech sentences and adding instrumental knowledge performs better than the existing and conventional approaches. The experimental results demonstrate the significant performance of the present CNN architecture by providing an 89.15% accuracy rate and a 10.56% WER for continuous and extensive vocabulary sentences of speech signals with different tones.

Fast and precise fruit classification or recognition as per quality parameter is the unmet need of agriculture business. This is an open research problem, which always attracts researchers. Machine learning and deep learning techniques have shown very promising results for the classification and object detection problems. Neat and clean dataset is the elementary requirement to build accurate and robust machine learning models for the real-time environment. With this objective we have created an image dataset of Indian fruits with quality parameter which are highly consumed or exported. Accordingly, we have considered six fruits namely apple, banana, guava, lime, orange, and pomegranate to create a dataset. The dataset is divided into three folders (1) Good quality fruits (2) Bad quality fruits, and (3) Mixed quality fruits each consists of six fruits subfolders. Total 19,500+ images in the processed format are available in the dataset. We strongly believe that the proposed dataset is very helpful for training, testing and validation of fruit classification or reorganization machine leaning model.

Because of the metaphysical shift in the technological era, the world is witnessing machine to machine (M2M) communication. Sensors are omnipresent, owing to which wireless sensor networks (WSNs) are developing as a key enabling technology for the Internet of Things (IoT). M2M interaction poses a severe threat to the lifetime of the network. Clustering plays an essential role in elevating the energy efficiency of the WSN. Particularly, uniform distribution of cluster heads (CHs) is crucial for achieving better network lifetime and uninterrupted sensing from the area under observation. For this, we propose an efficient two-fold clustering algorithm, namely second-fold clustering (SFC). In the first phase, the selection of CH is made based on zonal residual energy and zonal degree of connectivity. In the second phase, the leftover nodes known as isolated nodes are clumped. CH is selected based on the zonal degree of connectivity to ensure that energy is disbursed (almost) uniformly by all the nodes. This uniformity makes it suitable for integration with the IoT. To find out the efficacy of SFC, carried rigorous experimental study out where it is compared to two existing clustering algorithms. The simulation results show that SFC outperforms its counterparts. The results showed that SFC improved network lifetime by approximately 9.6% and 10.2% compared to regional energy-aware clustering scheme and Residual-Low Energy Adaptive Clustering Hierarchy (R-LEACH), respectively.

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks.

Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.

Abstract Remote sensing image object detection is widely used in civil and military fields. The important task is to detect objects such as ships, planes, airports, harbours and so on, and then it can obtain object category and position information. It is of great significance to use remote sensing images to observe the densely arranged and directional targets such as cars and ships parked in parking lots and harbours. The object detection task mainly includes object localization and classification. Remote sensing images contain large number of small objects and dense scenes due to the long shooting distance and wide coverage. Small objects occupy few pixels in the image, and they are easily miss‐detected. In dense scenes, the overlapping part of each object is large, so it is easy to detect objects repeatedly. The traditional small object detection methods deliver low accuracy and take long time. Therefore, object detection is very challenging. We put forward a novel deep learning‐based single shot multibox detector (SSD) model for object detection. First, we propose an improved inception network to optimize SSD to strengthen the small object feature extraction ability (FEA) in the shallow network. Second, the feature pyramid network is modified to enhance the fusion effect. Third, the deep feature fusion module is designed to improve the FEA of the deep network. Finally, the extracted image features are matched with candidate boxes with different aspect ratios to perform object detection and location with different scales. Experiments on DOTA show that the proposed algorithm can adapt to the remote sensing object detection in different backgrounds, and effectively improve the detection effect of remote sensing objects in complex scenes.

The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when implementing the feature channel. To increase classification accuracy, calibration is necessary. To increase the accuracy of channel recalibration findings, a multiscale channel recalibration model is provided, and the msSE-ResNet convolutional neural network is built. The multiscale properties flow through the network's highest pooling layer. The channel weights obtained at different scales are delivered into line fusion and used as input to the next channel recalibration model, which may improve the results of channel recalibration. The experimental findings reveal that the spatial recalibration model fares poorly on the job of classifying breast cancer pathology pictures when applied to the semantic segmentation of brain MRI images. The public BreakHis dataset is used to conduct the experiment. The network performs benign/malignant breast pathology picture classification collected at various magnifications with a classification accuracy of 88.87 percent, according to experimental data. The diseased images are also more resilient. Experiments on pathological pictures at various magnifications show that msSE-ResNet34 is capable of performing well when used to classify pathological images at various magnifications.

Currently, a smart city is an emerging field in urban cities to improve the quality of life through information and communication technology (ICT). In general, the traditional solid waste management (SWM) approach taken by municipal authorities for waste collection in urban areas must be enhanced to achieve the green and smart city goals. This article is primarily focused on the progress of ICT technologies in solid waste management. With that aim, a thorough analysis is carried out in the article, and from the analysis, we have identified distinct ICT technologies that have been implemented in SWM. The function, application, and limitations of each technology are presented in the article. From the review, it is concluded that the implementation of the Internet of Things (IoT) plays a significant role in minimizing the negative impact of waste on the environment. It is also identified that selection of the appropriate wireless communication protocol is critical during the implementation of IoT-based system because the sensor node at the bins is battery-powered. In addition, it is analysed that blockchain technology plays an essential role in realizing the waste–money model, as this model includes transactions between users and recyclers. Finally, in this article, we propose that the waste-to-money model, local network and gateway architecture, vision node, and customized prototype improve solid waste management system in terms of communication, energy consumption, and real-time monitoring.

This study observed the role of pH, chromium (VI) concentrations, temperatures and chromium reductases for of Cr (VI) reduction. Bacteria isolated from effluent were identified as Pseudomonas sp. by molecular analysis. Bacterial strain MAI4 showed significant reduction at pH 7 (84%), 100 μg Cr (VI)/ml (86%) and 35 °C (86%). Increase in time of incubation increased Cr (VI) reduction by P. entomophila MAI4 significantly and 120 h of incubation showed maximum reduction of Cr (VI). P. entomophila MAI4 also showed significant reduction of Cr (VI) (80%) in industrial waste water. Bacterial strain MAI4 reduced Cr (VI) into Cr (III) after 120 h which was detected as 70 ± 3 μg/ml in cell pellet and 30 ± 2 μg/ml in supernatant, respectively. Chromium reductase found in cell free extracts (CFE) reduced almost all Cr (VI) to Cr (III) compared to cell debris. Based on reduction under in vitro and in vivo conditions, Pseudomonas sp. MAI4 could be used as a bioremediator of Cr (VI) in contaminated effluents.

As a pharmacologically important heterocycle, oxadiazole paved the way to combat the problem associated with the confluence of many commercially available drugs with different pharmacological profiles. The present review focuses on the potential applications of five-membered heterocyclic oxadiazole derivatives, especially 1,2,4-oxadiazole, 1,2,5-oxadiazole, and 1,3,4-oxadiazole, as therapeutic agents. Designing new hybrid molecules containing the oxadiazole moiety is a better solution for the development of new drug molecules. The designed molecules may accumulate a biological profile better than those of the drugs currently available on the market. The present review will guide the way for researchers in the field of medicinal chemistry to design new biologically active molecules based on the oxadiazole nucleus. Antitubercular, antimalarial, anti-inflammatory, anti-HIV, antibacterial, and anticancer activities of various oxadiazoles have been reviewed extensively here.

Parkinson’s disease (PD) is a neurodegenerative disease that impacts the neural, physiological, and behavioral systems of the brain, in which mild variations in the initial phases of the disease make precise diagnosis difficult. The general symptoms of this disease are slow movements known as ‘bradykinesia’. The symptoms of this disease appear in middle age and the severity increases as one gets older. One of the earliest signs of PD is a speech disorder. This research proposed the effectiveness of using supervised classification algorithms, such as support vector machine (SVM), naïve Bayes, k-nearest neighbor (K-NN), and artificial neural network (ANN) with the subjective disease where the proposed diagnosis method consists of feature selection based on the filter method, the wrapper method, and classification processes. Since just a few clinical test features would be required for the diagnosis, a method such as this might reduce the time and expense associated with PD screening. The suggested strategy was compared to PD diagnostic techniques previously put forward and well-known classifiers. The experimental outcomes show that the accuracy of SVM is 87.17%, naïve Bayes is 74.11%, ANN is 96.7%, and KNN is 87.17%, and it is concluded that the ANN is the most accurate one with the highest accuracy. The obtained results were compared with those of previous studies, and it has been observed that the proposed work offers comparable and better results.