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Design and Simulation of a Multi-purpose Adjustable Modular Robot for Precision Agriculture
Global population growth, climate change, and labor shortages all represent substantial obstacles to meeting global food needs, and agricultural robots provide a possible solution. This work uses a survey to evaluate user behavior toward using agricultural wheel robots on small farms. The survey was conducted in various parts of India (Coimbatore, Bhubaneswar, and Silchar), where 250 large and medium commercial farmers participated. After the survey, a new robotic system architecture is a multi-purpose, adjustable, modular, and affordable robotic platform designed for precision agriculture. A unique feature is added to the design, which helps the robot to adjust by itself based on the row distances and crop heights. The software was designed using the Fusion 360, and simulation is carried out in GAZEBO and Robot Operating System (ROS). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Predictive Deep Learning Model is used to Identify Human Tissue-Specific Regulatory Variations For Diabetes
A predictive deep learning model is designed to predict a target variable based on a set of input variables to diagnose the tissue base regulatory variants in the human islets. In this article, the identification on human tissue-specific regulatory variations for Diabetes using the Pima dataset converting data into images, and then the input variables may include genetic data, gene expression data, and the proposed model uses Pima Indian dataset with the attributes such as age, sex, and BMI to predict whether a person has Diabetes or not. And this dataset is incorporated a combination two layered ResNet18 + ResNet50 and SVM classifier. The results obtained are compared with KNN, Naive bayes, SVM Random Forest, Gradient descent and the accuracy achieved is 98%. 2023 IEEE. -
The Efficiency of Ensemble Machine Learning Models on Network Intrusion Detection using KDDCup 99 Dataset
With the advent of data communication the increased usage of the technologies results in network intrusions and associated attacks. Consequently, the data violation rates are increased abundantly and that sacrifices Confidentiality, Integrity and Availability. This article focused on the network Intrusion Detection System (IDS) that detects various attacks and types. Machine learning (ML) has the potential to spot known-experience and Zero-day attacks. Consequently, the article has considered ML and ensembled models for the various attack classification. The major contributions of the current article are 3-fold. Initially, to understand the relevance and sufficiency of the dataset through exploratory data analysis. Second, the comprehensive understanding of the various attacks, its nature, various types and classifications and finally, the empirical analysis of the dataset through the potential of various ML models. The article utilized various discriminative models for the execution and all of the models have shown better accuracy. The tree-based ensemble model, Random Forest has outperformed the rest of the models with higher accuracy in the training and testing samples of 99.997 % and 99.969 % respectively. 2023 IEEE. -
Structural Health Monitoring Using Machine Learning Techniques
Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE. -
Analysis of Fraud Prediction and Detection Through Machine Learning
In today's world the rate of fraudulent activities has significantly elevated, because of which a need for a competent system is required. Among all the fraudulent activities insurance fraud has the most dominating rate of growth. Fraud studies have suggested, that upon identifying the similar characteristics of a fraudulent claim with the claimants, a system of forensic and data-mining technologies for fraud detection can be set up. In this, seek to define fraud and fraudster, and look at the types of fraud and followed by the consequences of fraud to financial systems. As fraud is getting widespread these days epically in the health care insurance system, dealing with this problem has become a necessity. Unsupervised machine learning algorithms such as K-Means clustering along with supervised algorithms used in machine learning, like support vector machines, logistic regression, design trees etc. can play a very vital role in binary class classifications, which would ultimately help in identifying and outreaching the desired goal of fraudulent detection. In the end, this paper specifies the best or the most appropriate model that could be used in the given dataset to produce the most accurate results, based on certain parameters of confusion metrics like accuracy, precision, and specificity. 2023 IEEE. -
Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
The complexity of classifying malware is high since it may take many forms and is constantly changing. With the help of transfer learning and easy access to massive data, neural networks may be able to easily manage this problem. This exploratory work aspires to swiftly and precisely classify malware shown as grayscale images into their various families. The VGG-16 model, which had already been trained, was used together with a learning algorithm, and the resulting accuracy was 88.40%. Additionally, the Inception-V3 algorithm for classifying malicious images into family members did significantly improve their unique approach when compared with the ResNet-50. The proposed model developed using a convolution neural network outperformed the others and correctly identified malware classification 94.7% of the time. Obtaining an F1-score of 0.93, our model outperformed the industry-standard VGG-16, ResNet-50, and Inception-V3. When VGG-16 was tuned incorrectly, however, it lost many of its parameters and performed poorly. Overall, the malware classification problem is eased by the approach of converting it to images and then classifying the generated images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Development and Evaluation of an Artificial Intelligence-Based System for Pancreatic Cancer Detection and Diagnosis
Due to its aggressive nature and late-stage manifestation, pancreatic cancer is a difficult illness to find and diagnose. The creation of a pancreatic cancer detection and diagnosis system based on artificial intelligence (AI) has the potential to increase early detection and improve treatment results. We have described the creation and assessment of an AI-based system in this paper that is intended for the identification of pancreatic cancer. A large dataset including a variety of medical pictures, including CT scans, MRI scans, and PET scans, as well as the related clinical information, was gathered for the study. With the help of the annotated dataset, a deep learning model built on convolutional neural networks was created. The proposed AI-based solution was then assessed using a separate test dataset made up of control cases and known pancreatic cancer patients. A significant effectiveness for the early diagnosis of the disease was shown by the systems excellent precision as well as sensitivity in identifying pancreatic tumors. The outcomes of this investigation demonstrate the promise of AI-based systems for pancreatic cancer detection and diagnosis. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
Evaluating the tissues responsible for complicated human illnesses is important to rank significance of genetic revision connected to features. In order to make predictions about the regulatory functions of geneticsvariations athwart wide range of epigenetic changes, this article introduces a Convolutional neural network (CNN) model upgraded filters and Deepsec framework incorporated with comprehensive ENCODE and Roadmap consortia have compiled a human epigenetic map that indicates specificity to certain tissues or cell types. Deepsec framework integrates transcription factors, histone modification markers, and RNA accessibility maps to comprehensively evaluate the consequences of non-coding alterations on the most important components, even for uncommon variations or novel mutations. By using trait-associated loci and more than 30 different human pancreatic islets and their subsets of cells sorted using fluorescence-activated cell sorting, annotations of epigenetic profiling were obtained (FACS) on a genome-wide scale. The proposed model, used '1492' publicly available GWAS datasets. My team presented that deepsec framework does epigenetic annotations found important GWAS associations and uncover regulatory loci from background signals when exposed to CNN-based analysis, offering fresh intuition underlying nadir causes of type 2diabetes. The suggested approaches are anticipated to be extensively used in downstream GWAS analysis, making it possible to assess non-coding variations and conduct downstream GWAS analysis 2023 IEEE. -
Machine Learning Observation on the Prediction of Diabetes Mellitus Disease
Diabetes disease has become as one of the common syndromes in many of the age groups. Diabetes can result in high blood sugar levels, a heart attack, or heart disease. This is one of the fastest developing illnesses, and it requires regular care. After seeing the doctor and being diagnosed, the patient is typically compelled to obtain their reports. Because this procedure is time-consuming and costly, we have the option of using ML approaches to solve this problem. Our research aims to foster a framework prepared to do all the more precisely foreseeing a patient's diabetes risk level. To develop models, classification methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Random Forest Classifier are employed. The results indicate that the techniques are quite accurate. The result showed that the prediction with the Logistic Regression model acquired the highest accuracy. 2023 IEEE. -
Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey
Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. 2023 IEEE. -
Node Overlapping Detection for Draggable Node-Based Applications
Node-based interfaces are user interfaces that are based on the concept of nodes, which represent individual units of functionality, and edges, which represent the connections between nodes. In a node-based interface, nodes are connected by edges to form a graph, which represents the data flow and relationships between different parts of the system. The Node overlapping detection technique is only for react flow version 11 and higher. Users having previous versions are not able to use that functionality. To detect the overlapping, based on the output of this library, several user-defined functions can be used to resolve to overlap. It will see the single-pixel overlap. Using this library, users can avoid Node and edge overlapping by creating custom edges. It is a simple JavaScript function currently used for reactjs. In the future, if any other script develops a draggable node-based flowsheet-creating feature, the user can use this library accordingly. 2023 IEEE. -
An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication
The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. 2023 IEEE. -
Impact of AI in Financial Technology- A Comprehensive Study and Analysis
Presently across the world, financial institutions strive tremendously hard to make financial services smarter to benefit from the advantages of digitization. To enhance client services, financial technology (Fintech) uses a variety of modern breakthrough technologies, including Artificial Intelligence (AI), 5G/6G, Blockchain, Metaverse, IoT, and others, in the financial sector. Many important financial services and procedures, including loans, authentication, fraud detection, quality control, creditworthiness, and several more, would be streamlined and improved by the adoption of technology. However, a need exists for the development of innovative financial products as well as the corresponding technological ecosystem. To launch Information and Communication Technology (ICT) alternatives, various major tech companies have placed their emphasis on Fintech. In this paper, we first explore the latest opportunities in Fintech. Furthermore, we also attempt to present a foundation of the Fintech accelerators, such as IoT, 5G, Digital twins, and Metaverse. Additionally, we also outline recommendations for future research directions in Fintech while looking forwards to potential difficulties. 2023 IEEE. -
Model and Algorithm of Multimodal Transportation in Logistics Transportation Based on Particle Swarm Optimization
With the rapid improvement of market economy and modern logistics technique, the logistics distribution link is receiving more and more attention, and the logistics distribution path question in distribution has become the core question in logistics distribution. Study the optimization of logistics distribution path. Logistics distribution path optimization needs to find an optimal distribution route with less distribution vehicles and the shortest total length of the path, and has the rapidity of distribution. The traditional algorithm takes a long time to search the optimal route, which makes it difficult to find the optimal distribution route, resulting in high logistics distribution costs. In order to quickly find the optimal distribution route and improve the quality of logistics service, a logistics model based on particle swarm optimization algorithm is proposed. The group is composed of several non-intelligent individuals or groups of individuals. Each individual's behavior follows certain simple rules and has no intelligence; Individuals or groups of individuals can cooperate to solve questions through certain principles of message exchange, thus showing the behavioral characteristics of collective intelligence. After research, the algorithm in this paper is effective and suitable for wide application in practice. 2023 IEEE. -
Optimal allocation algorithm of marketing resources based on improved random forest
Random Forest algorithm is an ensemble learning algorithm that classifies data by combining multiple decision trees. It has a wide range of applications and is not easy to overfit. It has a wide range of applications in medicine, bioinformatics, management and other fields. By studying the Cobb-Douglas sales function, it is found that it can only analyze the static allocation of marketing resources, but cannot describe the dynamic changes. Enterprise marketing resource management runs through the enterprise management from beginning to end. The research on marketing resource management is helpful for enterprises to grasp and control the whole process of marketing resource management from the overall and overall level, and has important theoretical value and reality for enterprise marketing management activities. significance. In the vast majority of enterprises in our country, the size of advertising promotion expenses and the number of salesmen is often determined based on the experience and subjective assumptions of decision makers, so it is difficult to say that they are optimized. This paper starts with determining the optimal advertising budget and the number of salespeople, and conducts applied research on the optimal allocation of marketing resources. 2023 IEEE. -
Application of CNN and Recurrent Neural Network Method for Osteosarcoma Bone Cancer Detection
The outlook for people with metastatic osteosarcoma at an advanced stage is poor. Osteosarcoma is the most frequent form of bone cancer in children and young adults. There is an urgent need for both advances in treatment tactics and the identification of novel therapeutic targets for osteosarcoma since the disease typically develops resistance to existing treatments. Cancer stem cells, also known as tumor stem cells, have been linked to the development and spread of cancer at multiple points in the disease's progression. Cancer stem cells are linked to treatment resistance and carcinogenesis, and recent studies have demonstrated that osteosarcoma shares these properties. The proposed methodology rests on the three pillars of preprocessing, feature extraction, and model training. During preprocessing, that the proposed approach eliminated isolated highlights to help us zero in on the trustworthy region. They use the wavelet transform and the gray level co-occurrence matrix to extract features. A CNN-RNN technique is used to evaluate the models. In terms of output quality, the proposed technique is superior to both CNN and RNN. 2023 IEEE. -
AI Based Variable Step Size Block Least Mean Square Filter for Noise Cancellation System
Most of the Active Noise Cancellation (ANC) systems working properly in low-frequency noises only. To make it suitable for isolating high-frequency noise, it needs an additional circuit which consumes more energy. This problem is mitigated in this study by designing a Variable Step size Block Least Mean Square (VSBLMS) filter which is suitable for an effective noise cancellation system. VSBLMS filter is designed with RCA to make a design area efficient and it is designed with a novel adder to achieve high speed as well as less energy consumption. The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed VSBLMS filter design mitigates the unwanted noises in various frequency bands. The proposed VSBLMS reduces the energy consumption by 9.32%, 27.63%, 13.53%, 11.80%, 10.71 %, 13.14% and 9.26% when compared with state of the art methods. 2023 IEEE. -
An Innovative Approach for Osteosarcoma Bone Cancer Detection based on Attention Embedded R-CNN Approach
The malignant bone tumor osteosarcoma. Any bone is at risk, but lengthy bones like the limbs are more vulnerable. Although the precise cause of this malignant growth is uncertain, experts concur that it is caused by changes to deoxyribonucleic acid (DNA) inside the bones. This can cause the breakdown of good tissue and the growth of aberrant, pathological bone. Osteosarcoma has a 76% cure rate if detected early and treated before it spreads to other parts of the body. An X-ray is the primary tool for detecting bone tumors. Bone X-rays and other imaging tests can help detect osteosarcoma. A biopsy should be performed for an accurate diagnosis. This is a time-consuming and tedious task that might be greatly reduced with the help of appropriate tools. Data preprocessing, segmentation, feature extraction, and model training are the four main pillars of the proposed approach. Unwanted noises can be filtered out with some preprocessing. Low-spatial-frequency and high-spatial-frequency components are separated using segmentation. The proposed approach employed Tumor Border Clarity, Joint Distance, Tumor Texture, and other features for feature extraction. Let's move on to A-Residual CNN model training. The success percentage of the proposed approach was 96.39 percent. 2023 IEEE. -
Statistical Analysis of Ecological Mathematical Model Based on Data Warehouse
Persistence of ecosystems, existence and stability of periodic and almost periodic solutions, and global attractiveness are important research contents in ecological mathematical theory. This article takes the ocean as an example to illustrate. The marine ecological model management system integrates marine technology, Internet technology and database technology. The purpose is to collect, organize and analyze mathematical models related to marine ecosystems, integrate them according to certain classification principles, and store them in the form of text. In the database, the query of the database according to the important parameters in the mathematical model or the classification of the mathematical model is provided on the Internet, and the queried mathematical model is displayed on the screen through the browser. This paper adopts the method of data warehouse. How to effectively use resources is an important aspect of whether to take the initiative in competition. Data warehouse can play the characteristics of information processing and has broad application prospects in the face of competition in the field of telecommunications. 2023 IEEE. -
Unraveling Women's Involvement in the Digital Realm: An Empirical Investigation
A virtual world in which communication is done through the electronic medium using the computer. This world allows the user to gain knowledge in the form of information. Even though it has a lot of advantages, there are enormous issues when an individual exists in cyberspace. At hand are several challenges to be overcome by individuals to protectively survive cyberspace. Such as various attacks, financial risks, online crimes, and more. In cyberspace, the targeted audience is womanhood of all eons. Educating and promoting awareness about the risk in cyberspace for women in society is the need of the hour. Each individual is facing risk while they are in a digital world. Stakeholders are not given alertness of the threat and its consequences. The paper analyzes the risk and consequences of women's society, as most victims are from that environment. In this, different risks faced and the consequences affected by women's civilization, are discussed. Also remedial measures are taken and should be taken are also deliberated. Supporting this, an online survey is taken from various groups of common people to know the status of women's civilization in the current era. 2023 IEEE.