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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 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 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 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 machine learning model to predict suicidal tendencies in students
[No abstract available] -
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 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 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 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 LSTM based model for stock price analysis and prediction
The share market in India is exceedingly unpredictable and volatile, with an infinite range of factors regulating the share market's orientations and tendencies; hence, forecasting the upswing and downturn is a difficult procedure. Because of several essential aspects, the principles of share market have always been unclear for shareholders. This study aims to significantly reduce the likelihood of analysis and forecasting with Long Short-term Memory (LSTM) model approach that is both resilient yet easy is still suggested. LSTM is a complete Learning Model that is a Predictive Method. Conversely, advancements in technology have opened the way for more efficient and precise share market forecasting in current times. Using the provided historical data sets, the results showed that the LSTM model has considerable potential for forecasting. 2023 Author(s). -
A Low-Complexity Multiplier-Less Filter Bank Based on Modified IFIR for the SDR Channelizer
Digital filter banks are extensively used in an SDR channelizer for channelization. The objective of this research work is to design a low computational complexity FIR filter bank for generating sharp transition width channels for SDR. The design of unified and variable bandwidth channels for SDR using the proposed structure is based on the modified IFIR filter structure and cosine modulation technique (CMT). The performance of the proposed structure is demonstrated with the help of an example. The results show that the multiplier complexity of the proposed structure is less than those of other state-of-the art methods. The optimization techniques are incorporated in this work to further reduce the complexity of the proposed structure. With the help of canonical signed digit (CSD), multi-objective artificial bee colony (MOABC) and shift inclusive differential coefficients (SIDC) common sub-expression elimination (CSE) optimization, the filter used in this structure is made multiplier-less. 2024 IETE. -
A Low Voltage and Low Power Analog Multiplier
In this research work, a low voltage analog multiplier has been realized through the utilization of a flipped voltage follower (FVF). The multiplier is characterized by its capacity to function at low power while exhibiting high gain. The exclusive use of transistors in its implementation renders it highly appropriate for fully integrated circuit applications. The multiplier has been developed using a supply voltage of 500 mV and an operating frequency of 25 KHz. The design consumes power of 8.23 uW. Moreover, a comparative study between the proposed multiplier and the conventional gilbert multiplier is presented in the paper. All simulations and layout designs have been conducted through the virtuoso analog design environment (ADE) of Cadence at 45 nm CMOS technology. 2023 IEEE. -
A low cost and high actuation speed 3D printed prosthetic arm /
Patent Number: 202241047867, Applicant: Sujatha A K. -
A low cost and high actuation speed 3D printed prosthetic arm /
Patent Number: 202241047867, Applicant: Sujatha A K. -
A low cost and high actuation speed 3D printed prosthetic arm /
Patent Number: 202241047867, Applicant: Sujatha A K. -
A longitudinal examination of the relation between academic stress and anxiety symptoms among adolescents in India: The role of physiological hyperarousal and social acceptance
Academic stress is a critical aspect of adolescent experience around the world, but particularly in countries with dense populations that lead to highly competitive college admissions. With a population of over one billion people, the competition for higher education in India is significantly high. Although research has shown that academic pressures are associated with anxiety in adolescents, this work is primarily cross-sectional. The current study examined academic stress and anxiety symptoms over time and assessed physiological hyperarousal as a mediator and social acceptance as a moderator of this relation in a sample of adolescents from India (N= 282, 1318 years, 84% female). Adolescents completed measures of academic stress, physiological hyperarousal, social acceptance and anxiety symptoms at two-time points, 5 months apart. Findings demonstrate direct effects of academic stress on changes in symptoms of generalised anxiety and panic, as well as indirect effects through physiological hyperarousal. Social acceptance did not moderate the relation, although it uniquely predicted changes in panic disorder symptoms over time. The findings of this study contribute to the scientific understanding of a potential mechanism through which academic stress leads to anxiety among adolescents in India. 2021 International Union of Psychological Science. -
A literature review on friction stir welding of dissimilar materials
Friction stir welding (FSW) employs a tool that does not require any filler materials; frictional heat is produced and performs a solid-state joining method. Severe plastic deformation causes to join similar and dissimilar materials without melting the workpiece at the welding line. Friction stir welding is the most recent friction welded joining processes with the most surprising features when welding various metal alloys, including magnesium, aluminium, copper, and steel. FSW is victorious of all the other conventional welding methods implied in many industrial applications like automobile, aerospace, fabrication, shipping, marines and robotics. It gives high-quality welds, energy input, and distortion are lower, better retention of mechanical properties; it is eco-friendly and can be performed less operating cost. This research work aims at the FSW process in Al-Cu alloys, highlighting:(a) Optimizing the welding process parameters, welding feed rate, tool rotation speed, (b) Evaluation of Electrical Conductance properties of joints, (c) Mechanical properties and metallography characteristics of joints. 2021 Elsevier Ltd. All rights reserved. -
A Lightweight Multi-Chaos-Based Image Encryption Scheme for IoT Networks
The swift development of the Internet of Things (IoT) has accelerated digitalization across several industries, offering networked applications in fields such as security, home automation, logistics, and quality control. The growth of connected devices, on the other hand, raises worries about data breaches and security hazards. Because of IoT devices' computational and energy limits, traditional cryptographic methods face issues. In this context, we emphasize the importance of our contribution to image encryption in IoT environments through the proposal of Multiple Map Chaos Based Image Encryption (MMCBIE), a novel method that leverages the power of multiple chaotic maps. MMCBIE uses multiple chaotic maps to construct a strong encryption framework that considers the inherent features of digital images. Our proposed method, MMCBIE, distinguishes itself by integrating multiple chaotic maps like Henon Chaotic Transform and 2D-Logistic Chaotic Transform in a novel combination, a unique approach that sets it apart from existing schemes. Compared to other chaotic-based encryption systems, this feature renders them practically indistinguishable from pure visual noise. Security evaluations and cryptanalysis confirm MMCBIE's high-level security properties, indicating its superiority over existing image encryption techniques. MMCBIE demonstrated superior performance with NPCR (Number of Pixel Changing Rate) score of 99.603, UACI (Unified Average Changing Intensity) score of 32.8828, MSE (Mean Square Error) score of 6625.4198, RMSE (Root Mean Square Error) score of 80.0063, PSNR (Peak Signal to Noise Ratio) score of 10.2114, and other security analyses. 2013 IEEE. -
A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer
Latest innovations in technology and computer science have opened up ample scope for tremendous advances in the healthcare field. Automated diagnosis of various medical problems has benefitted from advances in machine learning and deep learning models. Cancer diagnosis, prognosis prediction and classification have been the focus of an immense amount of research and development in intelligent systems. One of the major concerns of health and the reason for mortality in women is cervical cancer. It is the fourth most common cancer in women, as well as one of the top reasons of mortality in developing countries. Cervical cancer can be treated completely if it is diagnosed in its early stages. The acetowhite lesions are the critical informative features of the cervix. The current study proposes a novel feature engineering strategy called lesion feature extraction (LFE) followed by a lesion recognition algorithm (LRA) developed using a deep learning strategy embedded with a Gaussian mixture model with expectation maximum (EM) algorithm. The model performed with an accuracy of 0.943, sensitivity of 0.921 and specificity of 0.891. The proposed method will enable early, accurate diagnosis of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Legal Analysis of Cyber-Enabled Wildlife Offences in India: A Qualitative Case Study of Sea Fans (Gorgonia spp.) on YouTube
With the advent of the Internet, offences against threatened species have transitioned online. Such species are directly or indirectly traded on social media despite being protected under Indian wildlife law. A qualitative case study was undertaken to assess the preparedness of national law and policy in prohibiting such offences. Sixty-three YouTube links on sea fans in the Hindi language were accessed over 8 weeks, and the information generated by both content creators and audiences was gathered and categorized for analysis. The legal provisions were then interpreted and applied to assess the extent to which the parties involved could be held liable. Our investigation shows that of these video links, the content creators directly offered specimens for sale in 15.87% of instances, demonstrated physical possession of wild specimens in 23.81% of these posts, and were involved in both activities in 20.63% of the links, which in our analysis is explicitly prohibited under national law. The remaining 39.68% of video links merely disseminated information on the relevance or usage of species in occult or religious practices, for which no express legal provision currently exists. Certain indirect legal provisions were found to be relevant; however, there were challenges associated with their implementation. Even the liability of a social media company was found to be limited if it can be demonstrated that the company exercised due diligence. Therefore, there is a need to explicitly regulate online content that has the potential to drive an unlawful demand for protected species alongside the imposition of enhanced liability on social media companies. Such measures, coupled with community awareness, can reduce cyber-enabled wildlife offences committed through social media channels. 2024 Taylor & Francis Group, LLC.