Identification of Finger Vein Images with Deep Neural Networks

To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset.&nbsp

A systematic review A Conversational interface agent for the export business acceleration

Conversational agents, which understand, respond to, and learn from each interaction using Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Advanced Dialog Management, and Machine Learning (ML), have become more common in recent years. Conversational agents, also referred to as chatbots, are used to have real-time conversations with individuals. As a result, conversational agents are now being used in a variety of sectors, including those in education, healthcare, marketing, customer assistance, and entertainment. Conversational agents, which are frequently used as chatbots and virtual or AI helpers, show how computational linguistics is used in everyday life. It can be challenging to pinpoint the variables that affect the use of conversational agents for business acceleration and to defend their utility in order to enhance export company. This paper provides a summary of the evolution of conversational agents from a straightforward model to a sophisticated intelligent system, as well as how they are applied in various practical contexts. This study contributes to the body of literature on information systems by contrasting the different conversational agent types based on the export business acceleration interface. This paper also identifies the challenges conversational applications experience today and makes recommendations for further research

Identification and Classification for Diagnosis of Malaria Disease using Blood Cell Images

Machine Learning is a subfield of artificial intelligence that focuses on developing intelligent algorithms capable of learning from available data without requiring constant programming, enabling them to adapt to different environments based on current scenarios. These algorithms are crucial in making intelligent decisions and conducting thorough analyses to uncover intricate patterns concealed within the data. This study used multiple machine-learning classification algorithms to analyze patients' data based explicitly on input images containing parasite-infected and uninfected Malaria samples. AI techniques were utilised to measure the presence of parasites in the images. The image classification system was designed to accurately identify malaria parasites in blood images by generating image features related to color, texture, and cell and parasite geometry. A classifier based on SVM (Support Vector Machine) provided by Weka was employed to differentiate between parasite-infected and non-infected blood images. Through extensive experimentation, it was determined that SVM strategies exhibited significant relevance, achieving a cross-validation accuracy of 99.4% in the basic diagnosis of malaria fever. This finding holds great potential in assisting clinicians with accurate infection diagnoses

Diabetes Diagnosis through Machine Learning: An Analysis of Classification Algorithms

Diabetes is a serious and chronic disease characterized by high levels of sugar in the blood. If left untreated, it can lead to numerous complications. In the past, diagnosing diabetes required a visit to a diagnostic center and consultation with a doctor. However, the use of machine learning can help to identify the disease earlier and more accurately. This study aimed to create a model that can accurately predict the likelihood of diabetes in patients using three machine learning classification algorithms: Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). The model was tested on the Pima Indians Diabetes Database (PIDD) from the UCI machine learning repository and the performance of the algorithms was evaluated using various metrics such as accuracy, precision, F-measure, and recall. The results showed that Logistic Regression had the highest accuracy at 71.39% outperforming the other algorithms

Algorithmic as well as Space and Time comparison of various Deep Learning Algorithms

Deep learning is an artificial intelligence subfield within machine learning. Now- a-days, deep learning has been used in various applications like computer vision, natural language processing, speech recognition, social network filtering, neural machine translation, etc. Deep learning, Convolutional Neural Network (CNN) is a set of deep neural networks mainly designed for image analysis. Deep learning strong ability is mainly due to multiple feature extraction. In this pa- per, we will discuss and compare AlexNet,VGGNet-16,Residual Network(ResNet-50,101,152)

AN IMPROVED SVM AND CONVOLUTIONAL NEURAL NETWORK-BASED GARBAGE CLASSIFICATION SYSTEM (GCLS) AUGMENTED WITH TRANSFER LEARNING AND OBJECT DETECTION API

Garbage is a waste substance that is abandoned by people, generally owing to a perceived lack of utility. We are confronted with the massive amount of garbage generated by people every day that should be properly recycled, reused and repaired by the garbage management system. The first step after garbage collection is to separate or classify garbage into different categories such as glass, paper, plastic, etc. in order to reuse, recycle, repair and recover it. The existing classifiers can only classify garbage in three or six categories. We have designed and implemented a Garbage Classification and Labeling System (GCLS) using SVM and Convolutional Neural Network(CNN) that segregates garbage in eight classes and also label the objects in the image namely cardboard, leather, glass, metal, plastic, paper, rubber and  trash. Using transfer learning we have achieved up to 90.4% accuracy that is higher than the existing classifiers

Cloud Computing Services and Security Challenges: A Review

An architecture of computing that provides services over the internet on the demand and desires of users that pay for the accessible resources that are shared is refer as the cloud computing. These resources are shared over the cloud and users do not have to acquire them physically. Some of the shared resources are: software, hardware, networks, services, applications and servers. Almost every industry from hospitals to education is moving towards the cloud for storage of data because of managing the effective cost and time of organizing the resources physically on their space. Storage of data over the data centers provided in the form of clouds is the key service of the cloud computing. Users store their desired data on clouds that are publicly available over the internet and away from their boundaries in cost effective manner.  Therefore, techniques like encryption is used for obscuring the user’s information before uploading or storing to the shared cloud devices. The main aim of the techniques is to provide security to the data of users from unauthorized and malicious intrusions

A Comparative Study of Parallel and Distributed Big Data programming models: Methodologies, Challenges and Future Directions

According to a survey conducted in 2021, users share about 4 petabytes of data on Facebook daily. The exponential increase in data (called big data) plays a vital role in machine learning, internet of things (IoT), and business intelligence applications. Due to the rapid increase in big data, research in big data programming models gained much interest in the past decade. Today, many programming paradigms exist to handle big data, and selecting an appropriate model for a project is critical for its success. This study provides an in-depth analysis of big data programming models such as MapReduce, Directed Acyclic Graph (DAG), Bulk Synchronous Parallel (BSP), and SQL. We conduct a comparative study of distributed and parallel big data programming models and categorize these models into three classes: traditional data processing, graph-based processing, and query-based processing models. Furthermore, we evaluate these programming models based on different parameters like performance, data processing, storage, fault-tolerant, suitable language, and machine learning support. Finally, we highlight the benchmark datasets used for big data programming models and discuss the challenges of models along with future directions for the research community

Deep learning to predict Pulmonary Tuberculosis from Lung Posterior Chest Radiographs

Tuberculosis is one of the most dangerous health conditions on the globe. As it affects the human body, tuberculosis is an infectious illness. According to the World Health Organization, roughly 1.7 million individuals get TB throughout the course of their lifetimes. Pakistan ranks fifth among high-burden nations and is responsible for 61% of the TB burden within the WHO Eastern Mediterranean Region. Various methods and procedures exist for the early identification of TB. However, all methods and techniques have their limits. The bulk of currently known approaches for detecting TB rely on model-based segmentation of the lung. The primary purpose of the proposed study is to identify pulmonary TB utilising chest X-ray (Poster Anterior) lung pictures processed using image processing and machine learning methods. The recommended study introduces a unique model segmentation strategy for TB identification. For classification, CNN, Google Net, and other systems based on deep learning are used. On merged datasets, the best accuracy attained by the suggested method utilising Google Net was 89.58 percent. The recommended study will aid in the detection and accurate diagnosis of TB.&nbsp

An ONTOLOGY BASED APPROACH FOR TRAFFIC DENSITY ESTIMATION IN INTELLIGENT TRANSPORTATION SYSTEMS

Intelligent transportation systems are a group of smart technologies which are intended to make road journey more efficient. The ability of vehicles to share data with all aspects of the road infrastructure via diverse applications is critical to the success of these systems. In this paper, an ontology has been integrated for every vehicle to give them the ability to perceive for themselves. A framework of intelligent transportation system which enables complexity is required for the success of this traffic information exchange and representation of knowledge using the protégé. Proposed and implemented methodology for traffic density estimation is being used to create an ontology in the ITS domain