Customer Segmentation is the division of potential customer in a given market into discrete groups, which is based on customers having similar needs and buying characteristics such as gender, age, interests, and miscellaneous spending habits. While the most companies possess enough market knowledge to predict which customer segments are the company’s most profitable. The leaders of those businesses also know that scaling a business is not best left to guesswork. That’s why, in a customer segmentation process, it’s very important to develop customer segment hypotheses and variables, and then validate them with a well-developed, scientific research process. The project study and examines customer data and applies unsupervised machine learning methods such as K-Means and Hierarchical clustering to segment customers. The study also consolidates related work in the field of customer segmentation.

Keywords :

Author : Mr.G.MANTHRU NAIK, K.LASYA SREE,S.SIVA KRISHNA,R.VAMSI,SK.ABDUL HAFEEZ

Title : CUSTOMER SEGMENTATION OF A MALL USING MACHINE LEARNING

Volume/Issue : 2022;04(2)

Page No : 1-5

The field of bio sciences has advanced to a larger extent and has generated large amounts of information from Electronic Health Records. This has given rise to the acute need for knowledge generated from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of bio sciences. Heart is a condition in which the heart are damaged and cannot filter blood as they always do. Early detection of heart disease can improve the quality of life to a greater extent. This calls for a good prediction algorithm to predict heart disease at an earlier stage. Literature shows a wide range of machine learning algorithms employed for the prediction of heart disease. We use data pre processing, data transformation, and various classifiers to predict heart disease and propose the best Prediction framework for heart disease. The results of the framework show promising results of better prediction at an early stage of heart disease. The heart disease prediction and also the covid detection are also developed in the interface the input should be given according to the requirement. The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time

Keywords :

Author : DR. P. RATNA BABU,K.CHARAN, SK.MAAZ, SK.YASEEN, SK.BATHUL

Title : Effective Prediction Of Cardiovascular Diseases Using Machine Learning

Volume/Issue : 2022;04(2)

Page No : 6-11

Detection of image forgery is an important part of digital forensics and has attracted a lot of attention in the past few years. Previous research has examined residual pattern noise, wavelet transform and statistics, image pixel value histogram and other features of images to authenticate the primordial nature. With the development of neural network technologies, some effort has recently applied convolutional neural networks to detecting image forgery to achieve high-level image representation. This paper proposes to build a convolutional neural network different from the related work in which we try to understand extracted features from each convolutional layer and detect different types of image tampering through automatic feature learning. The proposed network involves five convolutional layers, two full-connected layers and a Soft max classifier. Our experiment has utilized CASIA v1.0, a public image set that contains authentic images and splicing images, and its further reformed versions containing retouching images and re-compressing images as the training data. Experimental results can clearly demonstrate the effectiveness and adaptability of the proposed network.

Keywords :

Author : Dr.G.SRINIVAS RAO,P.SAI LIKHITHA, K.KOTESWAR RAO, J.SURESH GOPI, B.MANASA

Title : Digital Image Forgery Detection Using Convolutional Neural Networks

Volume/Issue : 2022;04(2)

Page No : 12

Authentication dependent on passwords is utilized generally in applications for security and protection. Still, human actions, as an example, choosing bad passwords and contributing passwords in square measures are viewed as "the most fragile connection" in the Authentication chain. Maybe than discretionary alphanumeric strings, clients will pick passwords either short or significant for simple memorization. With web applications and versatile applications accumulation, individuals can get to these applications whenever and anyplace with various gadgets. This advancement brings extraordinary accommodation yet, in addition, builds the likelihood of presenting passwords to bear riding attacks. Attackers can notice straightforwardly or utilize outside recording gadgets to gather client’s qualifications. To avoid this sort of issue, we need another method of confirmation. Here, we can choose a graphical authentication method. The image password offers the best approach to sign on that is simpler than recollecting and composing along with simple passwords. You can sign in by tapping the right points or creating the right gestures over an image that you just select in advance. either short or from the word reference, instead of irregular alphanumeric strings. Human actions such as selecting bad passwords for new accounts and inputting wrong passwords in an insecure way for later logins are regarded as the weakest link in the chain of authentication. Shoulder surfing occurs when someone watches over your shoulder to collect valuable or personal information such as your password, ATM PIN, or credit card number, as you key it into an electronic device.

Keywords :

Author : Mr.G. MANTHRU NAIK, P.PRASANNA, KV.VENKATA SAI TEJA, P. BHANUPRAKASH, P.PRABHAKAR,

Title : GRAPHICAL PASSWORD AUTHENTICATION

Volume/Issue : 2022;04(2)

Page No : 17

Health care costs increases day by day. As there are a greater number of new viruses entering into people, there is a need to predict health charges. This type of prediction helps governments to make a decision regarding health issues. People also know the importance of health care costs. Machine Learning is a field that has an impact on every field. The health care system also uses machine learning models for several health-related applications. In this paper, we have done a predicate analysis on medical health insurance charges. We build a model to predict the medical insurance cost of a person based on gender. We collect the data set from Kaggle, which contains 1338 rows of data with the features age, gender, smoker, BMI, children, region, insurance charges. The data contains medical information and costs billed by health insurance companies. We applied various regression algorithms to this dataset to predict medical costs. For implementation, we used the Python programming language.

Keywords :

Author : Mr.M.A.Ram Prasad,T.Pavan Kalyan,D.Hari babU, SD.Zoyaferdose,S.Narasimha Rao

Title : Health Insurance Prediction Using Machine Leaning

Volume/Issue : 2022;04(2)

Page No : 23

In our modern era where the internet is ubiquitous, everyone relies on various online resources for news. Along with the increase in the use of social media platforms like Facebook, Twitter, etc. news spread rapidly among millions of users within a very short span of time. The spread of fake news has far-reaching consequences like the creation of biased opinions to swaying election outcomes for the benefit of certain candidates. Moreover, spammers use appealing news headlines to generate revenue using advertisements via click- baits. In this paper, we aim to perform binary classification of various news articles available online with the help of concepts pertaining to Artificial Intelligence, Natural Language Processing and Machine Learning. We aim to provide the user with the ability to classify the news as fake or real and also check the authenticity of the website publishing the news.

Keywords :

Author : Ms. B. RAMA JYOTHI, B. DIVYA MADHURI, G. JYOTHIRMAYEE,D. BHARATH KUMAR,M. KIRAN, T. PUSHKAR,

Title : Identification Of False Information Using Machine Learning

Volume/Issue : 2022;04(2)

Page No : 28

In India a countless number of children are reported missing every year. Among the missing child cases a large percentage of children remain untraced. This paper presents a novel use of deep learning methodology for identifying the reported missing child from the photos of multitude of children available, with the help of face recognition. The public can upload photographs of suspicious child into a common portal with landmarks and remarks. The photo will be automatically compared with the registered photos of the missing child from the repository. Classification of the input child image is performed and photo with best match will be selected from the database of missing children. For this, a deep learning model is trained to correctly identify the missing child from the missing child image database provided, using the facial image uploaded by the public. The CNN, a highly effective deep learning technique for image-based applications is adopted here for face recognition. Face descriptors are extracted from the images using a pre-trained CNN model VGG-Face deep architecture. Compared with normal deep learning applications, our algorithm uses convolution network only as a high-level feature extractor and the child recognition is done by the trained SVM classifier. Choosing the best performing CNN model for face recognition, VGG-Face and proper training of it results in a deep learning model invariant to noise, illumination, contrast, occlusion, image pose and age of the child and it outperforms earlier methods in face recognition based missing child identification. The classification performance achieved for child identification system is 99.41%. It was evaluated on 43 Child cases.

Keywords :

Author : DR.G. SRINIVASA RAO , S. MAHITHA, K. YASAWINI, CH. SHARATH SAI,K. BHARGV,SK. FATHIMUN ZAHARA,

Title : Missing Child Identification System using Deep Learning and Multiclass SVM

Volume/Issue : 2022;04(2)

Page No : 33

Software testing and quality assurance processes are facing major obstacles with the high complexity of modern software systems. Software Development Engineers in Test (SDETs) are responsible for designing, automating tests, identifying defects, and validating quality to ensure software reliability. But traditional testing methodologies usually take a lot of manual labour and fail to catch up with rapid software iteration cycles. Recently, specifically from Large Language Models (LLMs), new possibilities arise to optimize important aspects of software testing processes by capitalizing on what AI has to offer in terms of automation and decision support capabilities.This paper investigates from the perspective of AI-Augmented Software Development Engineers in Test (SDETs), on how Large Language Models can facilitate intelligent testing and defect triaging. This paper summarizes the possible applicability of LLMs in test case generation, automated test script creation, requirements analysis, documentation generation and defect classification/prioritization. The study also explores how LLMs can be utilized in failure analysis, root-cause identification, and bug resolution workflows within software quality engineering environments.The analysis elucidates some of the probable advantages offered by AI-assisted testing as follows: increased productivity, reduced testing effort, improved defect management and faster delivery of software. In parallel, some challenges like model hallucination, security issues, reliability issues and ethical consideration are explained. We compare traditional SDET practices with AIAugmented approaches to illustrate the transformational effects of generative frameworks on quality assurance of software.Results indicate that AI-Augmented SDETs might have significant potential in shaping the future of software engineering. Hence it is known to yield optimal testing process as well as software quality in addition faster development cycles by fusing human capabilities with smart language models. Finally, the paper highlights future research directions in autonomous testing systems, self-healing test automation and explainable-AI driven quality engineering.

Keywords : Software testing and quality assurance processes are facing major obstacles with the high complexity of modern software systems. Software Development Engineers in Test (SDETs) are responsible for designing, automating tests, identifying defects, and validating quality to ensure software reliability. But traditional testing methodologies usually take a lot of manual labour and fail to catch up with rapid software iteration cycles. Recently, specifically from Large Language Models (LLMs), new possibilities arise to optimize important aspects of software testing processes by capitalizing on what AI has to offer in terms of automation and decision support capabilities.This paper investigates from the perspective of AI-Augmented Software Development Engineers in Test (SDETs), on how Large Language Models can facilitate intelligent testing and defect triaging. This paper summarizes the possible applicability of LLMs in test case generation, automated test script creation, requirements analysis, documentation generation and defect classification/prioritization. The study also explores how LLMs can be utilized in failure analysis, root-cause identification, and bug resolution workflows within software quality engineering environments.The analysis elucidates some of the probable advantages offered by AI-assisted testing as follows: increased productivity, reduced testing effort, improved defect management and faster delivery of software. In parallel, some challenges like model hallucination, security issues, reliability issues and ethical consideration are explained. We compare traditional SDET practices with AIAugmented approaches to illustrate the transformational effects of generative frameworks on quality assurance of software.Results indicate that AI-Augmented SDETs might have significant potential in shaping the future of software engineering. Hence it is known to yield optimal testing process as well as software quality in addition faster development cycles by fusing human capabilities with smart language models. Finally, the paper highlights future research directions in autonomous testing systems, self-healing test automation and explainable-AI driven quality engineering.

Author : Rajasekhar Sunkara Senior Software Development Engineer in Test (SDET) | Quality Engineering Specialist

Title : The AI-Augmented SDET: A Study of Intelligent Test and Triage Systems Using Large Language Models

Volume/Issue : 2022;04(2)

Page No : 37-53