Advances in Machine Learning and Big Data Analytics II

Inhaltsverzeichnis

1. Frontmatter

2. Optimized Neural Networks Archetype for Prediction of Socio-economic Class of Women in India

   N. M. Jyothi, B. K. Rajya Lakshmi, Pavani Gonnuri, A. Pavani

   Abstract

   Women play a vital role in the national economy, and their Socio-Economic Class (SEC) is essential for their empowerment and the nation’s progress. This research aims to build an Artificial Intelligence (AI) model that classifies and compares the SEC of women across different regions of India. Numerous social and economic factors impact women’s SEC, including education, employment, and community support. We employed an Artificial Neural Network (ANN) model, which is enhanced by a new optimization technique involving Segmenting, Screening, Integrating, Enhancing, and Categorizing. This innovative method makes more precise feature selection possible and improves computational efficiency. The research outcomes reveal that this innovative technique significantly boosts the ANN’s performance, achieving an impressive classification accuracy of 99.34%. Compared to previous work, this model performs exceptionally well.

3. Improvement of UPFC Performance in Power Systems Using Artificial Intelligence

   Mamatha Deenakonda, Padma Jyothi Uppalapati, Sridevi Bonthu, Phanikumar Chittala

   Abstract

   To address various challenges in power generation and distribution such as power quality deterioration, total harmonic distortion (THD), stability issues, and transient response, FACTS (Flexible Alternating Current Transmission Systems) devices are commonly employed. The Unified Power Flow Controller (UPFC) plays a significant role among these devices. Artificial Intelligence techniques, particularly Artificial Neural Networks (ANNs), are utilized to enhance their performance. In this paper, we propose a novel approach combining an ANN controller with UPFC. This combination aims to significantly improve upon previous methodologies by reducing THD values and enhancing power system stability. In conjunction with a shunt filter and transformer, the ANN controller effectively manages power transient conditions, thereby demonstrating superior performance compared to conventional methods.

4. Classification of Parkinson’s Disease Using Machine Learning Techniques

   Satish Dekka, K. Narasimha Raju, D. Manendra Sai, M. Pallavi, Bosubabu Sambana

   Abstract

   Emotional speech and adverse social effects, such as stigma, dehumanization, and loneliness, are the main issues with Parkinson’s disease. This has a significant impact on the way of life of those who have Parkinson’s disease. The slow death or destruction of brain neurons is one of the known causes of Parkinson’s disease. As a result, dopamine is produced in the brain as a chemical messenger. Dopamine deficiency results in typical brain activity and a variety of modifications in how the body moves. Parkinson’s disease (PD) is mostly recognized by its signs and symptoms, such as a little tremor. Body movement is slowed down by tremors. Rigid muscles in this body weaken and lose automatic movements like blinking and smiling, which are additional symptoms. Previous researchers are still working on this problem, but they are having trouble using the right techniques to solve it. The suggested work utilized the MATLAB environment to develop three machine learning algorithms. The outcome indicates that the KNN algorithm has the highest accuracy in detecting Parkinson’s illness.

5. A Survey on Skin Cancer Detection Using Artificial Intelligence

   N. P. Patnaik M, Johan Jaidhan Beera

   Abstract

   Skin cancer ranks among the worst types of disease. For skin cancer to occur, there must be a mutation in the skin’s genetic material or a flaw in the DNA of skin cells. The importance of early skin cancer detection cannot be overstated since it can spread to other parts of the body and thus be less treatable later. It is important to recognize early warning signs of skin cancer because it is prevalent, has a high mortality rate, and is expensive to treat. Considering the gravity of these concerns, scientists have devised a number of methods for detecting skin cancer in its earliest stages. Skin cancer may be detected and classified as either benign or malignant based on several lesion factors, including but not limited to symmetry, color, size, form, etc. This study provides a comprehensive overview of AI methods for spotting skin cancer in its earliest stages. High-quality research publications on skin cancer diagnostics were reviewed. Tools, graphs, tables, methods, and frameworks portray research findings.

6. A Review of Ranking Approach to Rank Interval-Valued Trapezoidal Intuitionistic Fuzzy Sets

   S. N. Murty Kodukulla, V. Sireesha

   Abstract

   An Interval-valued trapezoidal intuitionistic fuzzy set (IVTrIFS) is a powerful tool in modeling uncertainty. It is a special type of Intuitionistic Fuzzy Set (IFS) and interval-valued intuitionistic fuzzy set (IVIFS), which has a consecutive domain of real numbers. An IVTrIFSs is distinguished by using three characteristic functions namely membership degree, non-membership degree, and hesitancy degree. Ranking is a challenging task, and every ranking method has its own significance and applicability. Since their inception in 1965, several researchers have proposed various ranking methods by using concepts such as Score and Accuracy, Centroids, Centre of Gravity, Distance/Similarity measures, Value and Ambiguity, etc. yet there is no specific ranking approach that is appropriate for all applications or provides adequate results in all situations. The Centre of Gravity (COG) method is one of the most popular defuzzification techniques of fuzzy mathematics. By utilizing the notion of the COG, one can ascertain the central tendency or representative value of a given fuzzy set. As it is derived by using area and centroids of any geometric representation, this method is particularly useful in fuzzy logic systems, fuzzy control, decision-making processes, and fuzzy data analysis. In this paper, we derive a ranking method for IVTrIFS using the Centre of Gravity.

7. An Empirical Evaluation of ResNet-SE-16 for Accurate Classification of Lung Cancer Using Histopathological Images

   P. Vishal, D. Kishore Kalyan Kumar, K. Venu Kiran Raju, K. Jayalaxmi Manojna, Mohan Mahanty

   Abstract

   Lung cancer is a leading cause of cancer-related deaths worldwide, accounting for approximately 25% of all cancer-related deaths. It is much more deadly than colon, breast, and prostate cancers combined. Early detection and treatment of cancer is crucial for a patient’s recovery. Radiologists use histopathological images to diagnose potentially infected lung regions, but this process can be time-consuming. Deep learning, a type of machine learning, can hasten this process by simulating how the human brain functions. Convolutional Neural Networks (CNNs) can quickly and accurately classify and identify the various kinds of lung cancer, improving patient treatment and survival chances. While many CNN models have been developed by researchers, they often require significant computational power and time. In this project, we developed an improved CNN model that classifies lung cancer types with high accuracy while requiring less computational power than other models. Using metrics like accuracy, precision, recall, and F1 score, our model obtained an accuracy of 98% during training and 97% during validation.

8. Design of EV Battery System Using Grid-Interface Solar PV Power with Novel Adaptive Digital Control Algorithm

   C. Sudhakar, K. Keerthi Reddy, D. Gopal Krishna, P. Pavan, N. Sreelatha, K. Rohini

   Abstract

   This inquiry aims to show a solar photovoltaic (PV) system that is linked to the grid and includes characteristics that allow for adjustments to be made to the power quality. Three major phases may be identified within the system. This system is employed to compensate for a range of power quality (PQ) issues, including harmonics, redundant reactive power, and load unbalancing, in addition to transferring power that is produced by a photovoltaic (PV) array to feed linear and nonlinear loads. This system also feeds linear and nonlinear loads. These are only some of the problems that our technology could be able to solve in the future. In addition to this, it can provide linear power to loads when this kind of power is required. A three-phase voltage source converter (VSC) is used so that the direct current (DC) electricity that is produced by the PV array may be converted into alternating current (AC). Alternating current, or AC, is the kind that is most often used. It is essential for the solar PV system that is connected to the grid to have an effective control strategy in order to simplify the transmission of active energy and to reduce the potential that power quality issues will occur. This research aims to illustrate how an adaptive generalized maximum Versoria criteria (AGMVC) controller may be used for a variable speed drive (VSC), which is a component of a solar photovoltaic energy conversion system. To guarantee effective utilization of the solar photovoltaic array, the maximum power point tracking (MPPT) method, which is founded on the perturb and observe algorithm, is used. The grid-integrated PV system’s experimental environment is first fabricated in the laboratory using an IGBT-based VSC and DSP (dSPACE DS-1202), and then the system itself is put together. Experiments are carried out to determine how effective the AGMVC control approach is. These experiments make use of a prototype that was developed inside the laboratory. This control method is evaluated in comparison with a variety of different conventional controllers, such as synchronous reference frame theory (SRFT) and instantaneous reactive power theory (IRPT), in addition to recently developed weight-based controllers, such as least mean square (LMS), least mean mixed norm (LMMN), and normalized kernel least mean fourth-neural network (NKLMFNN). To make a comparison between AGMVC and the control methods that have been discussed in the past, a number of criteria, such as fundamental weight convergence, steady state error, computational complexity, the requirement for phase lock loop (PLL), and the potential for providing harmonic compensation, are taken into consideration. The purpose of this comparison is to establish whether the AGMVC is more effective than the control approaches that were discussed before. In accordance with the IEEE-519 standard, the functionality of the system is evaluated and ranked in accordance with its performance during the testing. Harmonics, the maximum Versoria criteria, power factor correction (PFC), and solar photovoltaic are just some of the index terms that may be discovered in this section.

9. A Decentralized and Intelligent Approach for Suspicious Event Detection in Surveillance Range

   K. U. V. Padma, E. Neelima

   Abstract

   As the world becomes progressively more concerned about security, a gigantic amount of observational data is being generated by security systems. This data, produced on a daily basis, presents challenges for companies looking to store and manage it efficiently. The scattered nature of the data presents a critical challenge to data storage and analysis. One major concern is the security of data exchanged over devices, as any adversary can capture or alter the data, deceiving the reconnaissance system. Users can exchange files and data over the globe due to the scattered nature of the arrangement. Nevertheless, large files consuming a large amount of transmission bandwidth to upload and download over the web can benefit from the use of IPFS, which has rapidly gained notoriety due to its capacity to run the best of multiple protocols, including FTP and HTTP.

   Despite IPFS’s numerous focal points, there are security and access control issues, including a lack of traceability in how files are accessed. To address these issues, this article proposes a novel procedure that enhances IPFS with blockchain technology to give a straightforward audit trail. By leveraging blockchain as an asset, data reliability and source security can be improved, offering a clear way to trace all activity related to a specific file. This approach can enhance the security and integrity of the data, increasing confidence in the surveillance system’s ability to work effectively and securely.

10. Image Selection for Graphical Password Authentication

    P. Anjaneyulu, D. Priyanka, T. Chalapathi, B. Samanvi, B. Mounika

    Abstract

    One of the most crucial elements of information security is user authentication. User authentication is the primary strategy for ensuring the utmost factors for computer security. It gives the base for access control and user liability. Over time, among numerous types of user authentication systems, alphanumeric passwords have remained the most common. A graphical password is a type of authentication system that relies on the user’s selection of images presented in a graphical user interface (GUI), in a specific order. This approach, known as graphical user authentication (GUA), differs from the traditional system of using alphanumeric usernames and passwords, which is presently the most common way of authenticating computer users. This system has been shown to have significant disadvantages. For example, users tend to choose passwords that can be fluently guessed. On the other hand, if a password is delicate to guess, then it is frequently difficult to remember. To overcome this problem of low security, authentication styles are developed by experimenters, using images as a password. This explorative paper is a detailed examination of current graphical passwords, proposing a new proposition. Graphical passwords have been proposed as a volition to textbook-grounded schemes, based on the idea that humans tend to remember pictures more fluently than text. Also, pictures are considered to be more user-friendly.

11. Soft Computing for Visual Recognition Through Audio for the Visually Impaired

    S. R. K. L. Amulya, Vishakha Singh, Tummalapalli Sandeep, Surya Sasidhar, Rita Roy

    Abstract

    Visually impaired people are frequently unaware of outside obstructions and require assistance to avoid dangerous collisions. The sense of sight is the most fundamental aspect of human perception and plays. We propose an object recognition algorithm and assistance system that is very useful for their safety and quality of life. This project aims to use an available mobile device as a talking guide dog to aid those with vision impairments in outdoor navigation and inform them about obstacles. The proposed technology will lower the risks of obstacle contact by allowing users to move outside without stumbling, potentially informing the user what the objects are. The approach proposed uses algorithms of deep learning. CNN recognizes salient functions and captions of photos and converts written text to speech by detecting features through the broadcasted picture alongside its relevant caption, while the Gated Recurrent Unit (GRU) network acts as a tool that captions and describes the text detected from photographs. The predicted caption is transformed into an audio message. The proposed Convolution Neural Networks [CNNs] Gated Recurrent Units [GRUs] model has investigated the usage of numerous network architectures: Inception Net, Mobile Net, Xception Net, ResNet, and VGG16.

12. Diet Meal Plan Chatbot

    Saubhagya Jung Karki, Sarthak Pokhrel, Saugat Chand Thakuri, Anupam Pandey Chhetry, Yashpal Singh

    Abstract

    A healthy life is an important issue that has been affecting a large part of the human population as the obesity rate started to rise persistently while people have been rebuking it instead of finding a solution. We need to find out the method before having stinging defeat. Currently, AI-based software for personalized nutrition is getting hyped up for users to gain a healthy lifestyle. Chatbots empowered by Artificial Intelligence (AI) can be solutions to engage with humans in natural conversation and build relationships in the era of the Internet. Here in this paper, we present the knowledge of a Chatbot called “Diet-meal Chatbot” built by us to pitch it on the perfect line for the betterment of human beings. A diet meal chatbot is a conversational agent designed to have a tailored diet management plan. It uses NLP, a machine learning algorithm to understand user preferences and diet requirements, and suggests a balanced meal plan. It will be able to give them highly accurate physical exercise, healthy food style, and weight management style, for both males and females under whichever age. Chatbot will be able to suggest food for users based on their food intake, water intake, activity level, weight of users, age, and height as well.

13. Convolution Neural Networks

    K. Bhagyalaxmi, B. Dwarakanath

    Abstract

    Brain tumors are extremely dangerous and can lead to a very short life expectancy at their highest grade. Identifying brain tumors early is crucial for improving survival rates, but it poses a significant challenge for medical professionals. Noise and other environmental disturbances are more likely to appear in MRI. As a result, it is challenging for doctors to diagnose the tumor and determine its causes. Therefore, a system was proposed to detect brain tumors from images. In this procedure, the image is first converted into a grayscale then filters are applied to remove environmental interference and other noise from the image. The user has to select the image. The system processes images through various image-processing steps. However, in the early stages of a brain tumor, the edges in the images are not well-defined. To address this, a deep learning model incorporating CNN and FCNN has been developed to enhance the classification of tumor types and the associated preprocessing stages. This pre-trained network is capable of categorizing images into 1000 object categories. The fully connected layers are replaced with dense connections and a softmax activation function, enabling the classification of brain tumor images into four classes.

14. Segmenting MRI Images Using Federated Learning for Brain Tumor Detection

    T. L. Akshaya, K. Swetha, S. Pravalika, U. Chandrasekhar

    Abstract

    AI systems must be able to make use of large, diverse, and global information in order to build robust and efficient systems in the medical imaging field. To develop a global model, all these facts must be collected in one place, but this raises questions about privacy and ownership. In this study, we evaluated multiple federated learning methods for segmenting brain tumors. Federated learning utilizes all accessible data without storing or disclosing collaborators’ personal information on a central server. By appropriately integrating these model updates, a high degree of accuracy could be attained, but doing so increases the possibility that the shared model might accidentally leak the local training data. We have selected the best model from U-Net and DeepLabV3 to obtain the best efficiency.

15. Comparative Analysis of Fuzzy Regular Graph Properties

    Nagadurga Sathavalli, Tejaswini Pradhan

    Abstract

    We are going to discuss fuzzy regular graphs and their properties, such as fuzzy rules, cartesian products, and the If-Then rule. A very helpful tool for describing crucial aspects of a physical system with finite components is the fuzzy regular graph. In this work, we explore the fascinating results of the IF-THEN rule using a new parameter for both regular and completely regular fuzzy graphs via numerous examples, regular fuzzy graphs and completely regular fuzzy graphs are compared.

16. Effective Cloud Data Management by Using AES Encryption and Decryption

    Dogga Aswani, D. Jaya Kumari, Alluri Neethika, G. Sujatha, Praveen Kumar Karri, Sowmya Sree Karri

    Abstract

    Data security is a crucial issue in today’s digital world of communication. As we all know, hackers are everywhere these days, looking for valuable information that they can exploit for various purposes. When it comes to government data from any country, the risk doubles. As a result, a system or terminology must be in place to keep data secure at all times throughout transmission. Data protection can be achieved by converting the original data to some other unusable data, so that if that data is obtained, it must likewise remain in unusable bits. Previously, the DES block cipher algorithm was employed for security, but it had some limitations. DES has a key length of 56 bits and can be compromised by brute-force attacks. It is also subject to attacks that use linear cryptanalysis. So, we propose the AES Stream cipher technique for increased security, with key lengths of 128, 192, and 256 bits that will not be compromised by brute force assaults. It is more efficient than the DES cipher. In this proposed work, we aim to apply the AES method for encrypting and decrypting the data that is placed on the cloud server so that we may store the data effectively and efficiently for cloud users.

17. Prediction of Angina Pectoris

    D. Dakshayani Himabindu, Raswitha Bandi, Keesara Sravanthi, V. Sai Vandana, G. Uday Bhaskar

    Abstract

    Angina pectoris is the clinical term for chest discomfort associated with cardiovascular disease. It happens when the cardiac muscle is not getting enough oxygenated blood. Ischemia is a disorder in which some or all of the heart’s arteries are constricted or blocked. Many persons who are originally suspected of having angina have normal coronary angiograms, indicating that they do not, in fact, have angina. A feasibility study was conducted to determine the practicality of a preliminary screening test. Information on a variety of possible risk factors was collected for a large number of patients suspected of having angina, and then their angina status was documented. The major goal of this research was to see which health characteristics, if any, are linked to angina and whether a subset of them might be utilized to predict the dependent variable angina status. More specifically, being able to estimate the risk/probability that a person with a specific mix of these health indicators has angina would be beneficial. Furthermore, estimating the individual effects of relevant factors would be interesting.

18. A Novel Web Attack Recognition System for IOT via Ensemble Classification

    Preethi Bitra, Kandukuri Rekha Sai Kumar

    Abstract

    Machine learning is increasingly utilized in various fields such as biomedicine, information security, firewall protection, medical science, the Internet of Things (IoT), and mobile application security. Consequently, the demand for machine learning applications is growing. In this article, we apply machine learning to detect web or Internet attacks on IoT mobile devices. With the rapid growth in smartphone usage, protecting personal information and privacy has become a significant challenge. Impersonation is a major consequence of data leaks. Current preventive methods, like passcodes and fingerprinting, cannot continuously monitor usage to verify user authorization. Typically, once a user is authorized, they gain complete control of the device, and no further protection can prevent access to device data. To address this, we propose an ensemble machine learning model for detecting impersonation, based on a multi-view bagging strategy that collects sequential tapping information from the smartphone keyboard. This model continually authenticates the user while typing using sequential-tapping biometrics. By conducting multiple tests on our model, we evaluated it using the CLaMP (Classification of Malware with Portable headers) dataset from Kaggle. Our empirical and theoretical results demonstrate that our model outperforms other approaches, achieving a 6.42% equal error rate, 93.14% accuracy, and 95.41% H-mean using only the accelerometer and five keyboard taps.