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A Machine Learning Approach for Revving Up Revenue of Indian Tech Companies
This study addresses a critical gap in research by examining the effectiveness of various machine learning models in predicting revenue for Indian tech companies. The V.A.R, ARIMA, simple moving average, weighted moving average, and FB Prophet models were employed and their performances was compared. The findings demonstrate that FB Prophet consistently outperforms other models, exhibiting superior accuracy in revenue forecasting. This underscores FB Prophet's potential to offer precise revenue predictions, enabling companies to gain insights into their financial health, anticipate market trends, and optimize decision-making. Future research could further enhance accuracy by incorporating economic indicators, providing a more holistic view of revenue dynamics and empowering companies to make more informed strategic decisions. 2024 IEEE. -
A Machine Learning Entrenched Brain Tumor Recognition Framework
Brain tumor detection plays a significant role in medical image processing. Treatment for patients with brain tumors is primarily dependent on faster detection of these tumors. More rapid detection of brain tumors will help in the improvement of the patient's life chances. Diagnosis of brain tumors by doctors most commonly follow manual segmentation, which is difficult and time-consuming; instead, automatic detection is necessary. Nowadays, automatic detection plays a vital role and can be a solution to detecting brain tumors with better performance. Brain tumor detection using the MRI images method is an essential diagnostic tool for predicting brain tumors; the implementation for these kinds of detection can be done using various machine learning algorithms and methodologies. It helps the doctors understand the actual progression of the evolving tumor, allowing the doctors to decide how the treatment has to be given for that particular patient and measures required to follow up. Therefore, the intention is to create a framework to detect brain tumors in MRI images using a machine learning algorithm and analyze the performance of the brain tumor detection using sensitivity and specificity, which helps us to analyze how well the algorithm has performed in detecting the brain tumors accurately and develop a mobile application framework in which the MRI images can be directly scanned to know whether the cancer is present in a scanned MRI image or not. 2022 IEEE. -
A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People
In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. 2023 IEEE. -
A machine learning model for population analysis among different states in India which influences the socio, demographic and economic needs of society
In this work Data from 2011 census is taken to identify the state which influences more in Population census among the different states identified. The data is considered from Madhya Pradesh, followed with Utter Pradesh, then to Bihar, Bengal and Orissa. Similarly other case studies are also done for Southern Indian states and North Eastern States. Genetic algorithm will be tried to find the optimal location for the given study. A fitting function is calculated for the population data of 2011 using Lagrange Interpolation technique. This fitting function is given as input to Genetic algorithm to find the optimal state which have maximum influence in the population growth among different states of India as per the Case studies done. BEIESP. -
A machine learning model to predict suicidal tendencies in students
[No abstract available] -
A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring
Lane line detection is a vital component when driving heavy vehicles; this concept follows the path for driving a vehicle to prevent the risk of accidentally entering another lane without the drivers knowledge, which could result in an accident. To detect the lane, use frame masking and Hough line transformation with efficient machine learning algorithms, pre-processed and trained adequately for optimum accuracy as per the provided dataset to spot the white markings on both sides of the lane. Long-distance truck drivers suffer from sleep deprivation, making driving extremely dangerous while tired and they ignore the line markings and wander into the wrong lane. This chapter proposes a portable system that does not require any sensors or interference with the vehicles wiring system; instead, a system that fits on a windshield or any surface to monitor the actions of the driver, using computer vision and feature-extracted datasets within a trained neural network model using cameras. This driver-assisted system can detect drowsiness and give an alarm to wake up the driver by identifying the Region of Interest. These predictions are made based on eye movements, and the algorithm generates a score. The higher the score, the longer the time between alarms. 2024 Taylor & Francis Group, LLC. -
A Malicious Botnet Traffic Detection Using Machine Learning
Detection of incorrect and malign data transfers in the Internet of Things (IoT) network is important for IoT safety to observe an eye on and prevent unwelcomed traffic flow to the network of IoT. For it, Machine Learning (ML) strategic methods are produced by several researchers to prevent malign data flows through the network of IoT. Nonetheless, because of the wrong choice of feature, a few malign Machine Learning models differentiate especially the movement of malign traffic. Still, what matters is the problem that needs to be deliberated in-depth to select the best features for better malign traffic acquisition in the network of IoT. Dealing with the challenge, a new process was proposed. 1st, the metric method of selecting a novel feature called the proposed CorrAUC, and hinged on CorrAUC, a new highlight for choosing the Corrauc algorithm name is also being developed, designed hinged on the system folding filter features precisely and select the active features of the choose ML method using AUC metric. After that, we apply a combined application Order of Preference by Similarity to Ideal Solution Using Shannon Entropy (TOPSIS) built on a bijective set which is soft to verify selected features for identification of malign 1traffic in IoT network. We test our method using data set of Bot-IoT and 4 dissimilar ML classifiers. Practical outcomeanalysis showed that our proposed approach works as well and can achieve greater than 96% results on average. 2022 Wolters Kluwer Medknow Publications. All rights reserved. -
A Markovian risk model with possible by-claims and dividend barrier
A MAP/PH risk model with possible by-claims and a dividend barrier is considered. Along with the main claim, a by-claim also can occur with a certain probability but by-claims are settled only after an inquiry and hence delayed. The model is analysed considering associated Markovian fluid models under the original timeline and an auxiliary timeline. Systems of integro differential equations (IDE) are developed for the Gerber-Shiu function (GSF) and the total dividends paid until ruin. Explicit expressions are obtained for the GSF of the models without and then with the barrier. Expressions are also provided for the moments of the total dividends paid until ruin. A dividends-penalty identity is given. The method is numerically illustrated with a two-phase model and sensitivity analysis of the model is done by varying some of the parameters involved. 2023 Inderscience Enterprises Ltd.. All rights reserved. -
A mathematical approach to the study on alkylating agents
There are several classes of anticancer drugs, among which our study focuses on alkylating agents. As a chemical graph invariant number, topological index, has crucial role in predicting the physical, chemical, biological and toxicity properties of a molecule. Different versions of Zagreb indices correlate well with various physio-chemical properties of a molecule. We are analysing physio-chemical properties of the class of alkylating agents using various Zagreb indices. In this paper we are able to predict the physico-chemical properties of a molecule which is not yet discovered using the Zagreb class. 2022 Author(s). -
A Mathematical Correlation of Compressive Strength Among Silica, Alumina and Calcia Present in Composite Red Mud and Iron Ore Tailingbricks
Waste Red Mud generated from bauxite beneficiation in aluminium industry contains sodium oxide in minor amount along with silica and alumina in significant quantities. Waste iron ore tailings from beneficiation of iron ore in steel industry contain silica and alumina in significant quantities. A combination of both these materials in different amounts along with GGBS and lime addition resulted in complex alkali-activated reaction products consisting of (Si/Al), (Ca/Si) and (Ca/(Si+Al)) complexes which influence compressive strength of the test samples on curing for extended time periods at room temperature. Individual correlation coefficients of these complexes with compressive strength yielded high values with (Si/Al) and (Ca/Si+Al) ratios (0.92 and 0.96, respectively) while showing a poor correlation coefficient with (Ca/Si) ratio (0.88). A direct regression analysis between compressive strength and (Si/Al) ratio and (Ca/Si+Al) ratio indicated negative values with (Si/Al) ratios but positive values with (Ca/ (Si+Al)) ratios. It is therefore concluded that the addition of lime and GGBS (contributed from both GGBS and lime addition) resulted in Ca-Si-Al complex formations which are responsible for improved compressive strength of the samples. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A mathematical model that describes the relation of low-density lipoprotein and oxygen concentrations in a stenosed artery
The cellular activities of the endothelium layer between lumen and intima are significantly linked to the origin of the disease atherosclerosis. Three stages of atherosclerosis were investigated in this study (40%-mild, 50%-modest, and 60%-acute) concerning the coronary arterial segment. The essence of the hemodynamic factors like flow velocity, pressure, and wall shear stress has been investigated, as well as the interrelationships between them. At all degrees of stenosis, the biophysical relationship between convection-diffusion of low-density lipoproteins (LDL) and convection-diffusion of oxygen in the bloodstream is investigated. The Finite Element Methods (FEM) are used to solve the modeled partial differential equation systems. The method adopted is numerical in nature providing accurate graphical solutions to the framed systems. The physical effects of the deposition of LDL on the arterial wall, like a decrease in the diameter of the lumen, and toughening of the walls, are analyzed through the evaluation of the physical parameters. The study revealed that the deposition of LDL molecules in the post stenotic region leads to the depletion of oxygen in the region leading to the rapid dysfunctioning of the endothelial layer of the lumen-intima boundary. 2022 World Scientific Publishing Company. -
A Mathematical Model to Explore the Details in an Image with Local Binary Pattern Distribution (LBP)
Mathematical understanding is required to prove the completeness of any research and scientific problem. This mathematical model will help to understand, explain and verify the results obtained in the experiment. The model in a way will portray the mathematical approach of the entire research process. This paper discusses the mathematical background of proposed prediction of lung cancer with all the parameters. Processes involved analyzing the 2D images, basic quantitative method, from, related equation and fundamental algorithmic understanding with slightly modified versions of prediction are represented in the below section with how the local binary pattern distribution can be modified so that we get reduced run time and better accuracy in the final result. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Mental Health Epidemic?: Critical Questions on the National Mental Health Survey
Questions are raised about an approach towards psychiatric epidemiology, which directly imports models in medicine to count disorders of the mind to produce staggering evidence to the effect that 11% of Indians suffer from mental disorders. An alternative psychiatric epidemiology is needed, which relies on the principles of slow research, is value-based, and which defines mental health as an ethical and political problem. 2022 Economic and Political Weekly. All rights reserved. -
A Meta-Analysis on the Determinants of Online Product Reviews with Moderating Effect of Product Type
The technological advances in digital space have provided a renewed impetus for business to expand their footprint across digital modes. The growth of the internet and the ease of its access to the masses has encouraged many businesses to go online. Online e-commerce platforms make it easy to search, locate and place orders. Technology-assisted supply chains and fast delivery mechanisms ensure that users don't have to go elsewhere to fulfill their needs. To earn loyalty and customer satisfaction, e-commerce platforms have evolved into a sophisticated recommender system. It has evolved from just an informational source to a participative mode where users can share their experiences about their purchases. Customer values other user experiences more than the information provided by the seller. The presence of many conflicting and contradicting reviews can make the task of making rational decisions difficult for many users. Many studies were performed to understand what constitutes a review helpful and came up with different or mixed outcomes. The present study reviews the factors that influence online customer reviews helpful. Meta-analysis was performed to reconcile the mixed findings of different factors of online review helpfulness. The meta-analysis found that with the moderating effect of product type, factors like review length, readability, rating, reputation, and expertise positively correlate with helpfulness. Further, the customer finds moderate reviews more helpful in terms of polarity. Meta-analysis has a mix of findings for the selected data points in the study. The mixed findings include product type (search, experience, or other) and helpfulness measurement criteria. 2022 Kavita Rawat and Sunita Kumar. This is an open access article licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. -
A meta-analysis on the effects of haphazard motion of tiny/nano-sized particles on the dynamics and other physical properties of some fluids
Decline in the theoretical and empirical review of Brownian motion is worth noticing, not just because its relevance lies in the field of mathematical physics but due to unavailability of statistical technique. The ongoing debate on transport phenomenon and thermal performance of various fluids in the presence of haphazard motion of tiny particles as explained by Albert Einstein using kinetic theory and Robert Brown is further clinched in this report. This report presents the outcome of detailed inspections of the significance of Brownian motion on the flow of various fluids as reported in forty-three (43) published articles using the method of slope linear regression through the data point. The technique of slope regression through the data points of each physical property of the flow and Brownian motion parameter was established and used to generate four forest plots. The outcome of the study indicates that an increase in Brownian motion corresponds to an enhancement of haphazard motion of tiny particles. In view of this, there would always be a significant difference between the corresponding effects when Brownian motion is small and large in magnitude. Maximum heat transfer rate can be achieved due to Brownian motion in the presence of thermal radiation, thermal convective and mass convective at the wall in three-dimensional flow. In the presence of heat convective and mass convective at the wall, and thermal radiation, a significant increase in Nusselt number due to Brownian motion is guaranteed. A decrease in the concentration of fluid substance due to an increase in Brownian motion is bound to occur. This is not achievable in the case of high entropy generation and homogeneous-heterogeneous quartic autocatalytic kind of chemical reaction. 2019 The Physical Society of the Republic of China (Taiwan) -
A meta-ethnographic synthesis of lived experience of spouse caregivers in chronic illness
Social workers routinely work with chronically ill, providing support for long term care. Several qualitative studies describe the experiences of the person and carer in a chronic illness. There is a limited synthesis of these experiences to aid practice. The current review aims to present a synthesis of the experiences of the spouses of chronically ill persons reported in the literature. A comprehensive search of electronic databases was done, and the studies were selected using PRISMA guidelines. The selected studies were subjected to quality check using CASP guidelines and a score was assigned to each of those studies. Later, qualitative synthesis of the results of the selected studies was done using the principles of meta-ethnography. 2407 studies published between 19992019 were identified and 22 studies were included in the final synthesis. The number of participants in the studies reviewed was 309, with more representation of females. The reciprocal synthesis of these studies identified loss, change, caregiving and exhaustion, barriers in providing care, illness experience, coping, socio-cultural norms and support as common themes from the accounts of the participants. Continuity of change was identified as the core concept in the lived experience of the spouses of chronically ill persons. Illness, loss and Lived experience is proposed as a model of the lived experience of the spouses. Through this synthesis, the factors influencing the lived experience of spouse caregivers is understood, which can help social work professionals in the health sector in planning interventions for the spouses of chronically ill persons. The Author(s) 2021. -
A meta-heuristic based hybrid predictive model for sensor network data
Many prediction algorithms and techniques are used in data mining to predict the outcome of the response variable with respect to the values of input variables. However from literature, it is confirmed that a hybrid approach is always better in performance than a single algorithm. This is because the hybridization leads to combine all the advantages of the individual approaches, leading to the production of more effective and much improved results. Thus, making the model a productive one, which is far better than model proposed using individual techniques or algorithms. The purpose behind this chapter is to provide information to the users on how to build and investigate a hybrid Feed-forward Neural Network (FNN) using nature inspired meta heuristic algorithms such as the Gravitational Search Algorithm (GSA), Binary Bat Algorithm (BBAT), and hybrid BBATGSA algorithm for the prediction of sensor network data. Here, FNN is trained using a hybrid BBATGSA algorithm for predicting temperature data in sensor network. The data is collected using 54 sensors in a controlled environment of Intel Berkeley Research lab. The developed predictive model is evaluated by comparing it with existing two meta heuristic models such as FNNGSA and FNNBBAT. Each model is tested with three different V-shaped transfer functions. The experimental results and comparative study reveal that the developed FNNBBATGSA shows best performance in terms of accuracy. The FNNBBATGSA under three different V-shaped transfer functions produced an accuracy of 91.1, 98.5, and 91.2%. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
A metal-A nd base-free domino protocol for the synthesis of 1,3-benzoselenazines, 1,3-benzothiazines and related scaffolds
Efficient protocols have been described for the synthesis of 1,3-benzoselenazines, 1,3-benzothiazines, 2-aryl thiazin-4-ones and diaryl[b,f][1,5]diazocine-6,12(5H,11H)-diones. These transformations were successfully driven towards the product formation under mild acid catalyzed reaction conditions at room temperature using 2-amino aryl/hetero-aryl alkyl alcohols and amides as substrates. The merits of the present methods also rely on the easy access of rarely explored bioactive scaffolds like 1,3-benzoselenazine derivatives, for which well-documented methods are rarely known in the literature. A broad range of substrates with both electron-rich and electron-deficient groups were well-tolerated under the developed conditions to furnish the desired products in yields up to 98%. The scope of the devised method is not only restricted to the synthesis of 1,3-benzoselenazines, but it was also further extended towards the synthesis of 1,3-benzothiazines, 1,3-benzothiazinones and the corresponding eight membered N-heterocycles such as diaryl[b,f][1,5]diazocine-6,12(5H,11H)-diones. 2018 The Royal Society of Chemistry. -
A Metal-Free KOtBu-Mediated Protocol towards the Synthesis of Quinolines, Indenoquinolines and Acridines
An expeditious strategy has been developed for the synthesis of diverse quinolines, indenoquinolines and acridines using KOtBu-mediated reaction conditions. The designed process utilizes 2-aminoaryl carbaldehydes/2-aminoaryl ketones and methyl/methylene group containing ketones as readily available feedstock. The chemical transformation was affected at room temperature within a short duration of time to obtain diverse N-heterocycles yields up to 92 %. The established process also exhibits considerable functional group tolerance with an operational simplicity. 2024 Wiley-VCH GmbH. -
A method for identification of restarted radio sources from large radiosurveys
Active galaxies hosting radio jets can exhibit distinct active phases marked by two sets of radio lobes. Typically, these episodic radio sources have been identified through morphological observations. In addition, spectral characteristics-based methods are also employed wherever multi-frequency deep radio observations are available. However, these methods are inefficient in detecting restarted radio sources that do not exhibit a clear morphology. To address this, a method of using the spectral curvature (SPC=?150MHz1400MHz-?74MHz150MHz) to identify restarted radio sources is presented. This is based on the fact that restarted radio sources with significant remnant emission are expected to have concave spectra in contrast to the convex or straight spectra observed in most radio sources. We use available wide area radio surveys in the range of frequencies from 74MHz to 1.4GHz to search for episodic radio sources and to shortlist 9,405 sources based on the criteria of SPC?0.5. The candidates thus identified can be followed up for detailed morphological and spectral index studies. This method will find application in the automated identification of episodic radio sources in large radio sky surveys from telescopes like LOFAR and SKA. Indian Academy of Sciences 2025.