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Customer Behavior Analysis Using Unsupervised Clustering and Profiling: A Machine Learning Approach
Now-a-days, client conduct models are reliably established on information mining of client information, and each model is supposed to answer one solicitation at one point on schedule. Anticipating client conduct is a problematic and irksome task. Thus, making client conduct models requires the right strategy and approach. Right when an estimate model has been fabricated, it is challenging to restrict it for the motivations driving the advertiser, to pick the very thing displaying moves to make for every client or for the party of clients. Notwithstanding the multifaceted nature of this arrangement, most client models are completely fundamental. As the need might arise, most client conduct investigation models ignore such endless proper factors that the gauges they make are overall not altogether strong. This paper plans to encourage a connection rule mining model to expect client conduct using a typical electronic retail store for data combination and concentrate critical examples from the client conduct data. In this undertaking, a solo grouping of information on the customer's records from a regular food item company's data set will be played out. Customer segmentation is the act of clustering customers into bunches that reflect likenesses among customers in each group. Customers are separated into sections to advance the meaning of every customer to the business. To change items as indicated by unmistakable requirements and practices of the customers. It additionally assists the business with obliging the worries of various kinds of customers. Customers were clustered using a technique known as agglomerative clustering, which is a type of hierarchical clustering. Agglomerative clustering is a method for clustering data in a hierarchical order. It entails merging cases until you reach the appropriate number of clusters. The number of clusters to be produced is determined using the Elbow Method. 2022 IEEE. -
Performance Analysis of Nonlinear Companding Techniques for PAPR Mitigation in 5G GFDM Systems
Generalized Frequency Division Multiplexing (GFDM) is a 5G waveform contender that offers asynchronous and non-orthogonal data transmission, featuring several advantages, some of them being low latency, reduced out-of-band (OOB) radiation and low adjacent channel leakage ratio. GFDM is a non-orthogonal multicarrier waveform which enables data transmission on a time frequency grid. However, like orthogonal frequency division multiplexing and many other multicarrier systems, high peak-to-average power ratio (PAPR) is one of the main problems in GFDM, which degrades the high-power amplifier (HPA) efficiency and distorts the transmitted signal, thereby affecting the bit error rate (BER) performance of the system. Hence, PAPR reduction is essential for improved system performance and enhanced efficiency. Nonlinear companding techniques are known to be one of the effective low complexity PAPR reduction techniques for multicarrier systems. In this paper, a GFDM system is evaluated using mu law companding, root companding and exponential companding techniques for efficient PAPR reduction. The PAPR and BER graphs are used to evaluate the proposed methods in the presence of an HPA. Simulations show that, out of these three techniques, exponential companding was found to provide a trade-off between the PAPR reduction and BER performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Explainable IoT Forensics: Investigation on Digital Evidence
This research examines the relevance of digital forensics in the field of Internet of Things and describes how different forensics tools and software are used to investigate cybercrimes. It emphasizes the importance of IoT Forensics and how it's used to tackle cybercrimes. It also discusses on the challenges faced by IoT forensics and gives an insight into the recent advancements in the field. It gives a walkthrough about how digital forensics investigation is done in 'data stolen' or 'data deleted' scenario. An outline of research potential and problems in IoT forensics is given in this chapter. The main details of IoT forensics are described. In all stages of a forensic investigation, issues linked to IoT are highlighted along with the potential that IoT presents for forensics. An illustration of an IoT forensics case is given with appropriate analytics. A brief research overview is provided, with information on the important research directions and a review of relevant articles. Future research proposals are included in the chapter's conclusion. 2023 IEEE. -
Evaluation of Mechanical Properties and Microstructure of Polyester and Epoxy Resin Matrices Reinforced with Jute, E-glass and coconut Fiber
Composite manufacturing is a novel branch of science and often finds numerous applications in several industries. Some of them are sport, automobile, aerospace and marine industries. Some of the properties that can be highlighted are good mechanical properties along with stiffness and comparatively lighter weight. There is a continuous research in this area is as the constant pursuit to achieve greater performance by changing various materials and the combinations of those with various resins are experimented. In the current work, polyester and epoxy resins were reinforced with coconut, E-glass and jute fibers of 5-6mm length and were prepared by hand layup method. The fiber and resin were taken in 18:82 weight percentages. Post production of the composites they were subjected to various physical mechanical and microstructural studies to determine various properties. The morphological features were analyzed through the microstructural study done through scanning electron microscope. In comparison with the composites manufactured, The artificial fiber reinforced composite, E-glass fiber reinforced epoxy composites exhibited superior tensile strength, flexural strength, impact toughness and hardness values. Among the natural fiber reinforced composite, coconut fiber reinforced composites exhibited better tensile, impact and hardness than its counterpart jute reinforced composites. Thus the resins reinforced with E-glass fiber had the highest mechanical properties when compared with jute fiber reinforced composites (JFRC) and coconut fiber reinforced composites (CFRC). The cost effectiveness of the natural fiber reinforced composites is also an added advantage over the artificial fiber reinforced composites. 2018 Elsevier Ltd. -
Towards Sustainable Living through Sentiment Analysis during Covid19
Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyse the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis. The Electrochemical Society -
Sustainability & Comparative Impact Analysis of Coral reef bleaching in Indian context
An estimated value of 500 million of the population are directly benefited through coral reefs related jobs, food and defence of coastal areas. Coral reefs help to reduce wave energy by 97%. They help to protect the coastal areas from storms, floods and wave energy by 97%. Natural disasters such as Tsunami and erosion of coastal areas are protected by reefs. In this process, they help to protect the lives of many staying in the coastal areas including animals, properties, and other natural resources. There are reasons for reef deterioration like change of climate, high pollution, destructive fishing, bleaching of coral reefs is a big concern now worldwide. Severe coral bleaching is also reported in India. A significant rise in the surface temperature of Sea has become a critical reason for coral bleaching. This work attempts to Study the link between sustainability, SDG goals of 2030 given by United Nations and coral bleaching. In this work study period is focussed from 1985 to 2021 in the Indian coral reef bleaching areas. The Electrochemical Society -
eHED2SDG: A Framework Towards Sustainable Professionalism & Attaining SDG through Online Holistic Education in Indian Higher Education
To enable sustainable development of society it is essential to train the leaders and professionals of tomorrow. Developing a sustainable society and holistically developed future for budding professional is a significant objective of higher education Institutions. Every professional course learner is expected to utilize his skills, knowledge and time to contribute towards the development of society. Fostering sustainability in various domains of development is a requirement for Sustainable Development Goals (SDG). This research is inspired by multiple mental health related problems among professionals, inability to cope up with stress, quick dissatisfaction and frustrations, suicide, poor happiness quotient measured through multiple psychological tests and many other negative mental status which have paved the path for more serious approaches towards holistic development of young professions. This research addresses the SDG goal 4, Quality Education directly. Indirectly it can work as a catalyst to ignite the interest and create awareness about all the sustainable development goals. The Electrochemical Society -
Sentiment and Emotion Analysis of Significant Diseases in India and Russia
Healthcare organizations need this information to understand and treat the patient's concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper. 2023 IEEE. -
Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis
Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Evaluation of ML-Based Sentiment Analysis Techniques with Stochastic Gradient Descent and Logistic Regression
In recent times, along with the expansion of technology, the Internet also has flourished exponentially. World is more connected today not only through the technology, but also through sharing sentiments to express views, either be constructive or destructive in front of the world through social media. Twitter, Facebook, Instagram, etc., are being used as social media to reach the world. The study of understanding peoples emotions, intentions, attitudes from unstructured data is opinion mining/sentiment analysis. This is an application of NLP or text mining. In this paper, an attempt is made to realize sentiment analysis's multiple dimensions using approaches such as ML and NLP-based technqies like word frequency and TF-IDF. Using ML approach, experiments were conducted, and the performance of the predictions was visualized. Three different datasets are used. A comparison of logistic regression (LR) and stochastic gradient descent (SGD) algorithms are compared using two different document representation. An extensive comparison is carried out using three different types of dataset. Amazon instant video datasets, bank dataset and movie reviews datasets are being used for the same. Analysis of performance is accomplished by using different graphs. The results indicate that logistic regression performs better than stochastic gradient descent for movie review dataset by using word frequency and TF-IDF-based approach. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comparative Analysis of Sentiment Analysis Using RNN-LSTM and Logistic Regression
Social media analytics makes a big difference in the success or failure of an organization. The data gathered from social media can be used to get a hit type product by analyzing the data and getting important information about the need of the people. This can be done by implementing sentiment analysis on the available data and then accessing the feelings of the customers about the product or service and knowing if it is actually being liked by them or not. Tracking data of the customers helps the organization in many ways. This study was done to get familiarized with the concept of data analytics and how social media plays an important role in it. Furthermore, Web scraping of Twitter and YouTube data was done following which a standard dataset was selected to do the other analytics. The field of sentiment analysis was used to get the emotions of the people. Logistic regression and RNN-LSTM models were used to perform the same, and then, the results were compared. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A comparative analysis of opinions and sentiments on clean India campaign and sustainability goals of 2030
Human are blessed with natural intelligence. Artificial Intelligence can help human minds to make a best usage of machines to handle huge amount of data with accuracy and precision. AI has a widespread application in 21st century. Opinion mining is an application of artificial intelligence. The opinions expressed in social media can be extracted using python which can be used as an input for various machine learning algorithms to identify many patterns which can help policy makers to make effective policies. Clean India Campaign started in India with a set of goals to be achieved. Sustainability goals of 2030 given by United Nations puts light on many important aspects which need immediate attention in the next 9 years. Current pandemic Covid-19 has also triggered the necessity behind putting immediate attention for a better tomorrow. Without proper awareness programs, brainstorming knowledge cultivation, orienting minds towards the "what-why-where"aspects of sustainable growth in each sphere of life, aligning industrial development and digital era towards sustainable industrial development in digital era, sustainable economy, sustainable care of each natural resource; it is not easy to accomplish the sustainability goals of 2030 given by United Nations.This work emphasizes on the case study conducted as an initiative to motivate future policy makers to be aware of the different dimension of 2030 United Nations Agenda and the clean India campaign to take initiatives as a professional through the skills learned focusing on India. Realizing Individual social Responsibility can make a big difference in the planning and implementation of the goals and missions. Swachch Bharat Abhiyan (Clean India Campaign) started Swachch Bharat Mission-Urban (SBM-U) with a few objectives to make India Clean.This work has proposed two phases for analyzing opinions. This research have provided a methodology to apply AI to improve the opinion mining. The conventional opinion analysis is limited by reachability but the automated opinion analysis can be scaled up using artificial intelligence based applications. The uniqueness of the work lies in its focus on 'one-three verticals' in phase 1 of the methodology. Many prominent regions of India are considered as a part of the study. It helps us to provide a clearer picture across different regions of India. It also provide an avenue to list tasks to be done for each region and a set of ways which could be adopted by the future professionals and current stakeholders of higher education institute. Phase 2 focusses on more number of opinions collected from across the globe through digital platforms. 2021 Author(s). -
Exploring Sustainable and affordable Cancer Care using Artificial Intelligence
Now, in recent decades AI and ML have become a major part in developing and maintaining the healthcare system. Now, by using AI and ML in healthcare, it can provide a massive help for the healthcare workers.AI and ML help the healthcare workers for making better decisions, In some practical areas, it may take the place of human action for making decisions such as radiology, it can help to Gather medical knowledge or information from different journals, textbooks, or clinics which will help in reducing time for study and research. AI and ML help in predicting the early diagnosis of disease based on the patient's data and even help to prevent that dis-ease. Breast cancer is the most frequent category with an estimation of 2, 38,908 by 2025. Breast cancer is followed by lung cancer (1, 11,328); followed by mouth cancer (90,060). These statistics have triggered this research. Breast cancer is found in every one women among eight woman. Sustainable care shall help to fight with the disease. Sustainable care includes affordable cancer care and it's possible through early prediction of cancer. In this research we are using artificial intelligence based techniques for early prediction of cancer. Future direction of work will focus on usage of transfer learning and other models of AI-ML to help the society and mothers of nations to fight against the in-creasing spread of cancers. The Electrochemical Society -
Computational Modelling of Complex Systems for Democratizing Higher Education: A Tutorial on SAR Simulation
Engineering systems like Synthetic Aperture Radar (SAR) are complex systems and require multi-domain knowledge to understand. Teaching and learning SAR processing is intensive in terms of time and resources. It also requires software tools and computational power for preprocessing and image analysis. Extensive literature exists on computational models of SAR in MATLAB and other commercial platforms. Availability of computational models in open-source reproducible platforms like Python kernel in Jupyter notebooks running on Google Colaboratory democratizes such difficult topics and facilitates student learning. The model, discussed here, generates SAR data for a point scatterer using SAR geometry, antenna pattern, and range equation and processes the data in range and azimuth with an aim to generate SAR image. The model demonstrates the generation of synthetic aperture and the echo signal qualities as also how the pulse-to-pulse fluctuating range of a target requires resampling to align the energy with a regular grid. The model allows for changing parameters to alter for resolution, squint, geometry, radar elements such as antenna dimensions, and other factors. A successful learning outcome would be to understand where parameters need to be changed, to affect the model in a specific way. Factors affecting Range Doppler processing are demonstrated. Use of the discussed model nullifies use of commercial software and democratizes SAR topic in higher education. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sulfamic acid catalyzed grinding: A facile one-pot approach for the synthesis of polysubstituted pyrazoles under green conditions
A competent, rapid and simple grinding procedure for the synthesis of pharmacologically relevant polysubstituted pyrazoles catalyzed by sulfamic acid is reported via multicomponent reaction of substituted arylaldehydes, 4-nitrophenylacetonitrile, hydrazine hydrate, ethyl acetoacetate under solvent-free reaction conditions. In our reported protocol, four different reactants featuring diverse functional groups are assembled in one pot, enabling the synthesis of more diverse molecular structures in a facile manner. 2022 -
Sulphuric acid supported silica gel (H2SO4-SiO2) as an efficient catalyst for one-pot multicomponent synthesis of pyrano[2,3-c]pyrazol-amines under ultrasonication
In this study, the catalytic potential of a novel heterogeneous catalyst-sulphuric acid supported on silica gel (H2SO4-SiO2) has been assessed for the one-pot cyclo condensation reaction of aromatic aldehydes, 4-nitrophenylacetonitrile, ethyl acetoacetate and hydrazine/phenyl hydrazine to furnish poly functionalized pyrano[2,3-c]pyrazol-amine scaffolds under ultrasonication. Notably, within the framework of green chemistry, this divergent and step-economic approach has many benefits such as (i) use of water as solvent in the reaction, (ii) creation of up to five bonds in one sequence, (iii) avail of US irradiation as an efficient source of energy, (iv) application of nontoxic and reusable catalyst. Besides these, simple workup procedure, low catalyst loadings, shorter reaction time, high functional group compatibility, readily accessible starting materials and excellent yields without column chromatography render this protocol novel and greener towards the synthesis of poly functionalized pyrano[2,3-c]pyrazol-amines. 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Second International Symposium ''Functional Nanomaterials in Industrial Applications: Academy - Industry Meet''. -
Comparative Analysis and Development of Recommendations for the Use of Machine Learning Methods to Identify Network Traffic Anomalies in the Development of a Subsystem for User Behavioral Analysis
This article discusses various machine learning methods in order to conduct a more effective analysis of user network traffic using a subsystem for analyzing user behavior and detecting network anomalies, since there is a need to evaluate big data. The methods and techniques used to detect network anomalies are analyzed. In analyzing the methods and technologies used to detect network anomalies, a classification of anomaly detection methods is proposed. To solve these problems, different algorithms can be used, differing in specificity and, as a result, efficiency. The classification of machine learning methods for detecting network anomalies is considered separately, since machine learning algorithms will be the most effective for the task. Various criteria for evaluating the effectiveness of machine learning models in solving the problem of network traffic profiling are considered. In accordance with the specifics of the tasks of user recognition and network anomaly detection, the most appropriate criteria for evaluating the effectiveness of machine learning models have been selected: AUC ROC the area under the error curve. Four stages of the subsystem for analyzing user behavior and detecting network anomalies are highlighted. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
IOT based no-parking notifier system
Traffic congestion due to vehicles parked in No-parking zones has become a serious problem in major cities of India. Due to traffic congestion environment, economy and overall quality of life is affected. Hence it is high time to effectively manage the traffic congestion problem. With increase in number of vehicles, discipline in road regulation or traffic system becomes mandatory. The existing traffic system is very accurate but not efficient enough to monitor all the vehicles on the road. With the advent of new technology this problem can be tackled by using Wi-Fi enabled micro-controllers, RFID and cloud systems to monitor every vehicle on the road all the time. This becomes easy for the government in regulating its traffic rules with high efficiency without affecting the smoothness of the traffic. 2018 IEEE. -
Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models
Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. 2024 IEEE. -
An approach for document pre-processing and K Means algorithm implementation
The web mining is a cutting edge technology, which includes information gathering and classification of information over web. This paper puts forth the concepts of document pre-processing, which is achieved by extraction of keywords from the documents fetched from the web, processing it and generating a term-document matrix, TF-IDF and the different approaches of TF-IDF (term frequency Inverse document frequency) for each respective document. The last step is the clustering of these results through K Means algorithm, by comparing the performance of each approach used. The algorithm is realized on an X64 architecture and coded on Java and Matlab platform. The results are tabulated. 2014 IEEE.