Birds are an integral part of the biological ecosystem. However, spotting birds is an arduous task than hearing them chirping. Every bird species has a signature song relying on which ornithologists study birds in an ecosystem. Thus, bird-call recognition plays a pivotal role in identifying birds, understanding biological diversity and conserving them. Previously, a few research works have attempted to devise machine learning and deep learning techniques to identify bird-calls. However, most approaches inadequately focused on pre-processing and eliminating background noise from the audio input. In this paper, a fine-tuned EfficientNet-based ensemble model to classify avian species has been proposed with a novel tri-layered noise reduction approach. It has been augmented with a thresholding-based approach which discards the noisy samples altogether. Furthermore, a tri-point trade-off between the accuracy, model depth and the model parameter has been hypothesized and validated. The proposed methodology surpasses the existing state-of-the-art models yielding an accuracy of 0.97 and an F1-score of 0.95 on the Cornell Birdcall Identification task.

Our academic performance can differ due various to non-academic factors namely, self-motivation and mind stability where peace of mind plays a pivotal role; self-confidence after any kind of setback, environment and financial pressure, family support, and capability of an individual to make a combat. These non-academic factors may lead to stress affecting our academic performance. In this paper, we aim to assess the mentioned non-academic factors affect our academic performance. The paper is modeled on survey performed on college students and depending upon their responses related to these non-academic factors. We have carried out the work through statistical analysis and machine learning algorithms. Density bases Clustering algorithm is being used to cluster similar kind of performance and analyze how the factors differ from each other. Moreover, we also presented a comparative study between academic parameters such as attendance, interest in subject and travelling time in comparison to such non-academic performances. A rough set is also being created comprising the parameters which gives the best result among the students. The entire hypotheses are being tested on the various non-academic factors and how they impact academic performances and statistical calculations. Conclusion is drawn to check if non-academic factors really impact academic performances?

The arrival of COVID-19 took the very existence of human race for a toss. In countries like India, where the majority of the population is concentrated in the rural areas and are subject to an affordability and infrastructural constraint, cannot afford sophisticated COVID-19 tests. But, X-Ray is widely obtainable across both the rural and urban belts of our country and comes at an affordable cost, even free at the government hospitals. In the present research paper, we put forward a fusion-based DCGAN and CNN based neural net architecture which will generate synthetic COVID-19 infected lung X-Ray images from our fed data. Here we consider mainly two (2) output classes namely, malignant and benign. The novelty in this paper is that from the original X-Ray Image our model will generate a “predicted” image instantaneously using the DCGAN structure to understand the process of mutation. Also, the model predicts the class of the newly generated “predicted” image, whether it is COVID-19 positive or negative through the proposed CNN architecture. However, the paper that the success of deploying our model depends on the availability of the 5G network as the “predicted” X-Ray image along with the original X-Ray image of a patient needs to be transmitted to a central server from where it needs to be analyzed for further course of treatment as already specified. We have made an attempt to achieve the state of the art accuracy in our CNN model.

The most talked about disease of our era, cancer, has taken many lives, and most of them are due to late prognosis. Statistical data shows around 10 million people lose their lives per year due to cancer globally. With every passing year, the malignant cancer cells are evolving at a rapid pace. The cancer cells are mutating with time, and it's becoming much more dangerous than before. In the chapter, the authors propose a DCGAN-based neural net architecture that will generate synthetic blood cancer cell images from fed data. The images, which will be generated, don't exist but can be formed in the near future due to constant mutation of the virus. Afterwards, the synthetic image is passes through a CNN net architecture which will predict the output class of the synthetic image. The novelty in this chapter is that it will generate some cancer cell images that can be generated after mutation, and it will predict the class of the image, whether it's malignant or benign through the proposed CNN architecture.

In this chapter, the authors take a walkthrough in BCI technology. At first, they took a closer look into the kind of waves that are being generated by our brain (i.e., the EEG and ECoG waves). In the next section, they have discussed about patients affected by CLIS and ALS-CLIS and how they can be treated or be benefitted using BCI technology. Visually evoked potential-based BCI technology has also been thoroughly discussed in this chapter. The application of machine learning and deep learning in this field are also being discussed with the need for feature engineering in this paradigm also been said. In the final section, they have done a thorough literature survey on various research-related to this field with proposed methodology and results.

As it is said history repeats itself, in 2020, the world witnessed another epidemic with the name of novel corona virus (2019-nCoV). The dataset has been prepared through the information from John Hopkins University, WHO source, along with the data of Indian Government. In this paper, we will take a deeper view through the epidemiological data on corona virus affected patients. We have assessed various trends and discovered patterns through the data. We have studied the outbreak progression of the 2019-nCoV in India through exponential growth model. Various patterns are analyzed like gender affected, age group of people being affected the most and percentage people survived based on assessing delays between symptom onset and initialization of treatment by the professionals. Through modeling of maximum likelihood function, we have drawn an approximate conclusion on number of unreported cases of 2019-nCoV patients (initially as per data). Serial interval taken for other known corona-viruses (nCoV), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are used as an approximate measure to predict the number of unreported cases. The counts of unreported cases are more vital than its counterpart as it can cause severe outbreak if not checked. Along with that, we have proposed a LSTM-based architecture to predict the number of cases in India.

In this chapter, the authors take a closer look into the economic relation with cybercrime and an analytics method to combat that. At first, they examine whether the increase in the unemployment rate among youths is the prime cause of the growth of cybercrime or not. They proposed a model with the help of the Phillips curve and Okun's law to get hold of the assumptions. A brief discussion of the impact of cybercrime in economic growth is also presented in this paper. Crime pattern detection and the impact of bitcoin in the current digital currency market have also been discussed. They have proposed an analytic method to combat the crime using the concept of game theory. They have tested the vulnerability of the cloud datacenter using game theory where two players will play the game in non-cooperative strategy in the Nash equilibrium state. Through the rational state decisions of the players and implementation MSWA algorithm, they have simulated the results through which they can check the dysfunctionality probabilities of the datacenters.

One of the most talked about diseases of the 21st century is none other than cancer. In this chapter, the authors take a closer look to prevent cancer through machine learning approach. At first, they ran their classifier models (e.g., decision tree, K-mean, SVM, etc.) to check which algorithm gives the best result in terms of choosing right features for further treatment. The classified results are compared, and then various feature reduction algorithm is being used to identify exactly which features affects the most. Various data mining algorithms are being used, namely rough-based theory, graph-based clustering, to extract the most important features which influence the results. In the next section they take a look in the cancer analytics part. A simulation model has been designed that can easily manage the patient flow in OPDs and a bed rotation model also have been designed to give patients an insight that how much time they will spend in the queue. Further they analyzed a risk analysis model for chemotherapy treatment, and finally, an econometric discussion has been drawn in how it affects the treatment.

Cloud computing is the growing field in the industry, and every scale industry needs it now. The high scale usage of cloud has resulted in huge power consumption, and this power consumption has led to increase of carbon footprint affecting our mother nature. Thus, we need to optimize the power usage in the cloud servers. Various models are used to tackle this situation, of which one is a model based on link load. It minimized the bit energy consumption of network usage which includes energy efficiency routing and load balancing. Over this, multi-constraint rerouting is also adapted. Other power models which have been adapted are virtualization framework using multi-tenancy-oriented data center. It works by accommodating heterogeneous networks among virtual machines in virtual private cloud. Another strategy that is adopted is cloud partitioning concept using game theory. Other methods that are adopted are load spreading algorithm by shortest path bridging, load balancing by speed scaling, load balancing using graph constraint, and insert ranking method.

In this paper, we propose an ensemble-based transfer learning method to predict the X-ray image of a COVID-19 affected person. We have used a weighted Euclidean distance average as the parameter to ensemble the transfer learning model viz. ResNet50, VGG16, VGG19, Xception, and InceptionV3. Image augmentations have been carried out using generative adversarial network modelling. We took 784 training images, and 278 test images to validate our model accuracy, and the accuracy of our proposed model was around 98.67% for the training data set and 95.52% for the test data set. Along with that, we also propose a genetic algorithm optimized classification algorithm, to analyze the symptoms of COVID-19 for low, medium, and high-risk patients. The accuracy for the optimized set overshadowed the accuracy of un-optimized classification, and the optimized accuracy is as high as 88.96% for the optimized model. The novelty of this paper lies in the bi-sided model of the paper, i.e., we propose two major models, and one is the genetic algorithm optimized model to analyze the symptoms for a patient of varied risk and the other is to classify the X-ray image using an ensemble-based transfer learning model.

The main objective of this chapter is to take a deeper look into the infrastructural condition of the hospitals across the districts of West Bengal, India. There is a liaison between various variables and the infrastructural growth of the public healthcare centres. In this chapter, the authors have formed a panel data from the year 2004 – 2017, consisting of 17 districts across West Bengal. They have assessed the random effect model on the data to choose their respective hypothesis. A Bayesian risk analysis had also been carried out on the mortality rate of the patients on which factors it depends. Next, a Poisson distribution model is being fit to get some insights into the data. Afterward, they predicted the number of patients who will arrive in 2020 and the shortfall of hospitals is also being projected. The remedies to these have also been suggested in that section. At last, they carried out an econometric analysis in the healthcare domain and took a closer look at how healthcare expenditure affects our focus variables performance.

The huge amount of data burst which occurred with the arrival of economic access to the internet led to the rise of market of cloud computing which stores this data. And obtaining results from these data led to the growth of the “big data” industry which analyses this humongous amount of data and retrieve conclusion using various algorithms. Hadoop as a big data platform certainly uses map-reduce framework to give an analysis report of big data. The term “big data” can be defined as modern technique to store, capture, and manage data which are in the scale of petabytes or larger sized dataset with high-velocity and various structures. To address this massive growth of data or big data requires a huge computing space to ensure fruitful results through processing of data, and cloud computing is that technology that can perform huge-scale and computation which are very complex in nature. Cloud analytics does enable organizations to perform better business intelligence, data warehouse operation, and online analytical processing (OLAP).

The number of reported cases in India has been scaling up in geometric progression despite the stringent lockdown norms imposed to keep people indoors since late March. Interestingly, on 31st March there were 1117 affected cases, 33,610 on 30th April and 511,478 till June 26—an unprecedented rise in the numbers. In the present research article, we propose a differential equation-based mathematical model for modeling India’s COVID-19 that incorporates the lockdown effect while looking at the future predictions in terms of the spread and the extent to which lockdown has been effective in India. We have estimated the growth of COVID-19 across India using modified SIR modeling, which is a Compartmental model in Epidemiology. Further, the use of SIQR model to estimate the growth of this disease across the country. Also, a constant factor has been introduced in the model to measure the number of corona-affected patients count due to any accidental mass crowd gathering. Along with that, we analyse the pollution level of India under three conditional scenarios viz. Pre-Lockdown, During Lockdown and After Lockdown. From the epidemiological evidences, it is evident that several pollutants like pm 2.5, NO2, SO2, O3, CO noxious effects of pollution. Here we will analyse the basic contributing factor of pollution and which majorly impacts AQI. We will also visualise the change of AQI in the context of the season or a particular time, i.e. during the festive season and Diwali pollution highly increases and it continues till April. During COVID-19, to avoid the contamination and spreading of the virus, Govt of India declared Lockdown and due to this all the industrial works gets stopped and the reduction of vehicular waste also reduced and thus the concentration of pollutants (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu {\text{g}}/{\rm{m}}^{3}$$\end{document}μg/m3) decreases immensely. It can be interpreted that due to closure of industries and decreases in the number of vehicles, the concentration of the pollutants decreases thus it can be said that COVID-19 is a blessing to nature. But after reopening, i.e. unlock 1 the concentration, increases rapidly and immensely and from the reports, it is evident that in only in ecological regions, there is an increase of 400%. Thus after unlock, people are avoiding social gathering maintaining social distance preferring own vehicle rather than the public vehicle. Also, sales of the cycle are increasing promoting greenery and the use of pollution-free vehicles. Is the Nightmare and pandemic situation helps to maintain ecological balance? In this paper we try to analyse these facts keeping different factors into consideration, we will deal with the trend and seasonality of AQI and predict using time series analysis and LSTM. We will build a model which will give a satisfactory output of the quality of air and how the pollutants hamper human health using mathematical models. The novelty in the paper is the comparative study of the models under two scenarios viz., what could have been the figure without lockdown and social distancing and with lockdown and social distancing, along with the AQI Analysis on the same said scenarios. Simultaneously, this is correlated with the predictions for the rise of air quality level.

Groundwater is the primary source for drinking water and irrigation in India, and from last few years, due to population burst across the nation, there is a sharp decline in the groundwater level (availability). There is a constant pressure balance among the groundwater and seawater level so that contaminated water cannot seep into and due to lowering level there is an alarming situation for water contamination across India. In this paper, we aim to find the liaison between groundwater level and ground contamination condition through LSTM predictive modeling. The proposed algorithm for groundwater prediction is based on conditional approach through deep LSTM modeling and the ground contamination is calculated using an aggregated scoring approach modeled using Euclidian distance concept. Lastly, a correlative study is being provided to analyze the liaison in between the said variables. There is a high negative correlation among the said variables indicating loss of groundwater level is increasing the contamination level across the taken zones. The experiment has been carried out on the data across the three eastern Indian states, viz. West Bengal, Odhisa, and Bihar for a time span from 2004 to 2017.

In this paper, we aim to identify rice disease based only on the most vital features which affect the rice plants. Our dataset consists of 120 diseased images of three types of rice disease, namely bacterial leaf blight, brown spot, and leaf smut. Various image processing techniques have been applied to those images, and various features are extracted from those images. In total, we extracted around 29 features (24 color features and 5 texture features). To reduce the complexity of the classification algorithm, we have proposed a rough set theory-based feature selection method that selects the subset of the whole dataset which have same or near predictive results compared to whole dataset. The method also uses Pearson correlation coefficient to measure the similarity among the features. The classifier accuracy obtained using reduced dataset proves that our model worked successfully. Accuracy of classifiers, namely k-nearest neighbor, support vector machine, and naive Bayes on whole dataset are 82.54, 80.36, and 77.43% and on reduced dataset created by our proposed algorithm are 92.32, 87.24, and 89.67%, respectively.

“Who questions much, shall learn much, and retain much.”-Francis Bacon English Philosopher This quotation by Francis Bacon conveys that people, who question more, learn more in comparison to their peers and it is quite natural that one has to be present in the class to bring forth his or her question. In today’s world of advancing technology, absence from class is a major challenge not only in West Bengal, India but across the globe. The growth in this trend of being absent in class eventually leads to poor grade. In this paper, we aim to find a relation between absence, interest of student in subject, staying in hostel or travelling regular to college and the final grade of the student. Also, it will be our aim to find out how the above mentioned factors affect the grade of that particular student. A sample of 86 students is taken into account to predict the outcome. The data regarding the interest in each subject is collected from the individual students to calculate while the attendance and grades are collected from the college authority. The statistical models used in findings are students t-test, Pearson’s correlation and regression model. Hypothesis generated for each independent variable affecting the target variable Grade and through statistical calculations, conclusions are drawn whether or not they really affect or it is simply a myth. This study is beneficial for both, the college authority as well as the students to create awareness among them regarding the drawbacks of not attending the classes and not creating interest in the subject.