User Requests Research Articles

Online prediction tools for melanoma survival: A comparison.

Breslow thickness, patient age and ulceration are the three most valuable clinical and pathological predictors of melanoma survival. A readily available reliable online tool that accurately considers these and other predictors could be valuable for clinicians managing melanoma patients. To compare online melanoma survival prediction tools that request user input of clinical and pathological features. Search engines were used to identify available predictive nomograms. For each, clinical and pathological predictors were compared. Three tools were identified. The American Joint Committee on Cancer tool inappropriately rated thin tumors as higher risk than intermediate tumors. The University of Louisville tool was found to have six shortcomings: a requirement for sentinel node biopsy, unavailable input of thin melanoma or patients over 70 years of age and less reliable hazard ratio calculations for age, ulceration, and tumor thickness. The LifeMath.net tool was found to appropriately consider tumor thickness, ulceration, age, sex, site and tumor subtype in predicting survival. The authors did not have access to the base data used to compile various prediction tools. The LifeMath.net prediction tool is the most reliable for clinicians in counselling patients with newly diagnosed primary cutaneous melanoma regarding their survival prospects.

Integration of Small Cells with Smart Antennas in Macrocells for Improving the Performance of Mobile Telephone Networks

In macrocellular networks, there are areas where it is difficult to have satisfactory connectivity. In practice, the delays for sending or downloading multimedia applications, or even consulting web applications can become quite long.
 
 One solution is to deploy cells with smaller ranges called small cells inside the macrocell, closer to subscribers in order to boost network capacity. Only, with the instability of electrical energy, the small cells may not be powered properly. A way should therefore be found to further reduce the energy consumption of small cells in order to facilitate their deployment in Cameroon. To do this, we opted for the solution of smart antennas to be integrated into the small cells; antennas that transmit at the request of users and according to their needs.
 
 In this work, we design and build an application that illustrates the performance of small cells with three key innovations:
 
 
 Smart antennas make it possible to estimate users’ directions and deliver a maximum radiation pattern in those directions, while minimizing interference.
 The capacity of a heterogeneous network is boosted compared to that of a homogeneous network.
 The signal attenuation in free space in a micro cell is a function of the user-small cell distance. The application that we propose is therefore a relevant decision-making tool for the engineering and planning of mobile networks.

MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms

This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energyconscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests, subject to an average UAV power constraint, it is shown that the optimization problem can be cast as a semi-Markov decision process, and exhibits a multiscale structure: outer actions on radial wait velocities and terminal service positions minimize the longterm delay-power trade-off, optimized via value iteration; given these outer actions, inner actions on angular wait velocities and service trajectories minimize a short-term delay-energy cost; finally, rate adaptation is embedded along the trajectory to leverage air-to-ground channel propagation conditions. A novel hierarchical competitive swarm optimization scheme is developed in the inner optimization, to devise high-resolution trajectories via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms (MAESTRO-X) via scalable policy replication, enabled by a decentralized command-and-control network augmented with: (1) spread maximization to proactively position UAVs to serve future requests; (2) consensus-driven conflict resolution to orchestrate scheduling decisions based on delay-energy costs including queuing dynamics; (3) adaptive frequency reuse to improve spectrum utilization across the network; and (4) a piggybacking mechanism allowing UAVs to serve multiple ground users simultaneously. Numerical evaluations show that, for user requests of 10 Mbits, generated according to a Poisson arrival process with rate 0.2 req/min/UAV, single-agent MAESTRO offers 3.8× faster service than a high-altitude platform and 29% faster than a static UAV deployment; moreover, for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7× faster than a successive convex approximation scheme; and remarkably, a single UAV optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.

Prospects for developing digital telecommunication complexes for storing and analyzing media data

In today's digital world, saturated with data flows, universal multifunctional systems are developing, capable of solving various problems related to optimizing the use of available computing resources. A distinctive feature of such systems is the heterogeneity of incoming flows of user requests due to the multifunctionality of modern information systems, expressed in supporting various multimedia services on a single platform. Data heterogeneity and large volumes of data create many problems related to the speed of digital systems and data storage security. The solutions can be found in artificial intelligence (AI) technologies, particularly machine learning. Therefore, development and implementation of digital telecommunication complexes for storing, processing, and forming a dynamic flow of multiformat data using AI technologies are becoming more relevant. This paper aims to identify trends and prospects for developing these complexes, and develop proposals on their perspective characteristics. The authors focused on review the experience of Russian organizations developing multi-object analytics systems and analyze the technical and functional characteristics of existing systems. The result of the review and analysis is a table with a comparison of the technical characteristics of existing complexes and proposals for characteristics that are promising for further implementation.

Conversion of NLP to SQL Using Machine Learning Approach

Abstract: Now days data is increasing rapidly. There are so many new database tools and technologies are rowing, therefore we can store large data, but the problem is that the technology or an interface which can process data and display the data as per the user request is not familiarized with many of the people. It means many people don’t have proper knowledge of handling database. Actually, NLP to SQL conversion is part of machine learning. The main idea of NLP to SQL conversion is to generate SQL query from natural language. This technique is useful for accessing data from database without having prior knowledge of SQL. This technique can be used by many common people. In this system input is simple English text and query is generated by using POS tagger in python. In this project we are going to implement a system which will generate SQL query from natural language.

Guardians of Privacy: Understanding the European Union's Framework for Biometric Data Protection

With the growing prevalence of biometric technology, individual traits are being stored in an increasing number of databases. Users must maintain a high degree of awareness regarding biometric data security. It's important not to provide biometric details without thorough consideration of its necessity, scrutinizing the existing security measures, and understanding the track record of any entity requesting such information. The laws and regulations concerning this matter vary widely depending on the user's location. The European Union is renowned for its stringent personal data protection laws and robust security standards, imposing reporting responsibilities on any entity that collects and requests user data. This article will delve into the regulations and policies surrounding biometric data protection within the European Union.

Hyper contour cryptography with master Birch‐Brown Paruvala‐SLSTM model for enhanced healthcare security

AbstractBlockchain is a reliable ledger database that may be used to store various private healthcare data, but uploading and sharing data is difficult due to privacy concerns. Hence, to overcome these issues, a novel HCCLO with master Birch‐Brown Paruvala‐SLSTM model has been proposed to store various patients' medical data and shield it from threats. Hyper Contour Cryptography with Lepidoptera Optimization (HCCLO) has been proposed in this model, which encrypts data to reduce unwanted time stamp creation and prevent private key theft. Also, the Master Birch‐Brown‐Parulava NN design is introduced to prevent a false identity that leads to a data breach, and it allows for private exchange and enhances framework reconciliation, acting as an immune response to information breaks in the chain and preventing data breaches from spreading further. Then, the SLSTM layer implant RF Classifier is introduced to categorize data as user requests and attacks, safeguarding the blocks against changeability caused by malware attacks. The proposed approach offers minimal block mine time, maximum accuracy, and healthcare data security.

Python-Based Desktop Assistant with Voice Recognition

Voice assistants offer a more intuitive and efficient means of interacting with computers, reducing the need for input devices like keyboards and mice. By incorporating natural language processing technology, they can understand complex user requests and perform a wide range of tasks. Adding emotion recognition and personalization features can tailor responses to users' preferences and moods. Multi-language support and continuous listening further enhance user experience. Voice assistants have come a long way in terms of development, from the first digital laptop to current devices with impressive processing capabilities. However, there is still potential for further improvement. Voice assistants offer an opportunity to reduce hardware costs and the space required to accommodate input devices. They are a significant breakthrough in human-computer interaction and will continue to become more powerful and efficient as technology evolves. The development of voice assistants reflects the advancements in science and technology. Their applications can extend beyond basic input control and offer users an improved computing experience. Voice assistants will undoubtedly become a key component of our daily lives

Joint optimization of recommendation and caching based on user preference prediction

AbstractThe development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and relieve the burden of the backhaul link. Moreover, the introduction of recommendation mechanisms can reshape user's request and improve cache hit ratio. However, the optimization of recommendation and caching decisions is highly dependent on the users’ preference information for files. Here, a joint optimization algorithm of recommendation and caching based on users’ preference prediction with multiple base stations cooperative caching is proposed. To improve the caching efficiency, the Deep Crossing model is adopted to predict users’ preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, an optimization problem to minimize the total transmission delay of the system is formulated. Then, the NP‐hardness of the proposed optimization problem is proved and it is decoupled it into three sub‐problems, namely recommendation, user access and caching optimization sub‐problems. Simulation results show that the proposed algorithm can effectively reduce the total transmission delay of the system.

Constraint‐aware and multi‐objective optimization for micro‐service composition in mobile edge computing

AbstractAs a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro‐services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro‐services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro‐services constructed by ignoring the processing capabilities of different micro‐services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro‐services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro‐service composition. In order to improve edge resource utilization and user experience on micro‐service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro‐services composition problem in MEC as a constrained multi‐objective optimization problem. Then, a micro‐service composition optimization method M3C combining graph search and branch‐and‐bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment.

Effectual Text Classification in Data Mining: A Practical Approach

Text classification is the process of setting records into classes that have already been set up based on what they say. It automatically puts texts in natural languages into categories that have already been set up. Text classification is the most crucial part of text retrieval systems, which find texts based on what the user requests, and text understanding systems, which change the text in some way, like by making summaries, answering questions, or pulling out data. Existing algorithms that use supervised learning to classify text automatically need enough examples to learn well. The algorithms for data mining are used to classify texts, as well as a review of the work that has been done on classifying texts. Design/Methodology/Approach: Data mining algorithms that are used to classify texts were talked about, and studies that looked at how these algorithms were used to classify texts were looked at, with a focus on comparative studies. Findings: No classifier can always do the best job because different datasets and situations lead to different classification accuracy. Implications for Real Life: When using data mining algorithms to classify text documents, it's important to keep in mind that the conditions of the data will affect how well the documents are classified. For this reason, the data should be well organized.

Mutual Information-Based Modeling for Services Dependency

Web services composition has drawn a great attention in computing industries to build complex and large systems. However, web services composition modeling has major challenges, including dependency determination, complexity, user requests dependency, handling cycles within a composition, service redundancy and scalability concerns. The service dependency graph (SDG) between services in a repository should be accurate to ensure the quality of composition and the associated user requests' responses. Despite of the crucial importance of accurate dependencies for adequate web services compositions, current modeling approaches do not provide any metric to evaluate the dependency between services for quality estimation. In this paper, the Mutual Information-based Services Dependency (MISD) model is proposed as a graph-based modeling approach, independent to any given user request. It constructs services dependency graphs based on Web Services Mutual Information (WSMI), a proposed modified version of mutual information as the dependency metric, along with other criteria for an accurate, efficient dependency evaluation. It finds the optimum composition representing the structure of the web services composition in a repository rather than the path of given user requests. The experimental dependency analysis emphasizes the efficiency of the generated OC and accuracy of the constructed SDG to be 76% and 86% higher than the state-of-the-art models respectively. The time cost to build the SDG and to find the OC is reduced dramatically up to 99% for different public repositories compared to the state-of-the-art studies as the number of user requests increases.

Study of the accuracy problem of the integrated reporting assurance

The article examines the problem of ensuring the reliability of information in an integrated report. The need for sustainable development has led to the emergence of integrated accounting and analytical systems that combine financial and non-financial information to support transparency of information and trust in the enterprise. Integrated accounting and analytical systems can facilitate processes such as planning, forecasting, analysis, and task execution. Integrated reporting is a forward-looking report that presents strategy, management, performance indicators, development prospects and business risks, and requires the creation of a quality internal audit service to provide control of actions and technical support for the analysis and consolidation of information. The requests of information users require transparency and reliability from integrated reporting, therefore, to ensure the fulfillment of these requirements, the enterprise must use special mechanisms and procedures, an internal audit of integrated reporting in particular. Ensuring reliability can be implemented through both external and internal audits. Companies are working to provide external guarantees on the quality of integrated reports through external independent audits and internal audits, which have the effect of reducing information asymmetry and facilitating risk assessment. Internal audit helps ensure the accuracy and reliability of data in integrated reporting, taking on a strategic role in preparing the company for the implementation and use of this report, which helps to ensure a quality level of information confirmation and ensure the trust of interested parties.

Needs and requests of users of information on the innovative capital of the enterprise: the problem of relevance

The need to solve the problem of the relevance of accounting information regarding the innovative capital of the enterprise has been grounded. Two main types of relevance of financial reporting (cost, credit) have been identified and characterized. The classification of subjects forming information needs and requests regarding the innovative activity of the enterprise has been carried out. The information needs of various groups of subjects regarding the enterprise's innovative capital (internal users, external users, accounting researchers, economist researchers, representatives of consulting companies, professional evaluators and analysts) have been analyzed. The reasons for the loss of relevance of accounting information in the part of the enterprise’s innovative capital generated by the current accounting system in terms of two main components of relevance (predictive value, confirmatory value) have been grounded. Two main groups of proposals have been identified to eliminate the problem of loss of relevance of accounting information in the part of innovative activity of the enterprise (within the existing accounting model, within the new accounting model).

E-Commerce Website based on Chatbot

Abstract: Nowadays, traditional markets are gradually are being replaced by many online marketplaces. Fierce competition in the online market demands excellent service from both sellers and buyers, so many online stores offer his 24-hour service. This service would certainly cost a lot of money if done manually. Chat bots can be used as a solution for automated shopping online. Second, the bot must be able to answer accurately and quickly. In this study, we propose an intelligent chatbot system based on AIML (Artificial Intelligence Markup Language) that can be used as an e-commerce assistant. Input questions from users are processed in her three phases: parsing, pattern matching, and AIML crawling the data. In the AIML process, user requests fall into his three categories: general questions, calculations, and inventory checks. Where compute requests intercept the order and payment process

A delayed eviction caching replacement strategy with unified standard for edge servers

The current explosion of user traffic compels Internet service providers to cache contents at edge servers, so as to reduce the response time of user requests. To deal with such vast amounts of content in edge servers with limited storage, an efficient caching replacement strategy is necessary, consisting of admission and eviction processes. Facing such a large number of user requests and limited storage of edge servers, traditional caching replacement strategies have encountered two major problems. Firstly, they conduct eviction process immediately after cache miss, which ignores the fetching time, during which there may still be multiple access requests to the evicted contents. Secondly, most existing solutions treat admission process and eviction process separately, with different standards, some contents might be fetched and evicted back and forth, seriously affecting user experience. To solve these two problems and improve caching performance, we design two caching modules, Delayed-Eviction and Unified-Standard, and integrate them together into Adele framework. By conducting extensive simulations using real traces, we demonstrate that Adele can improve the hit rate by 31% compared with the state-of-the-art solutions.

Let Us Create Multiple IoT Device Controller Using AWS, ESP32 And C#

Purpose: The Internet of Things (IoT) has revolutionized how we interact with our environment by allowing various devices to connect and communicate. In this paper, we propose the development of a multiple IoT device controller using Amazon Web Services (AWS), ESP32, and C# programming language. The proposed system will allow users to control and monitor multiple IoT devices simultaneously through a centralized platform. The ESP32, a low-cost wifi module, will interface with the IoT devices and transmit data to the AWS IoT Core. The C# programming language will be used to develop the user interface and handle user requests. The proposed multiple IoT device controller using AWS, ESP32, and C# programming language is expected to provide a practical solution for managing and controlling multiple IoT devices, improving user experience, and advancing IoT technology. The code used in this paper is ready to download for continuing the research work. Design/Methodology/Approach: The proposed system uses AWS IoT Core to manage the devices and their data. The ESP32 module connects to the IoT devices and sends/receives data to/from AWS IoT Core. The device controller application is developed using C# programming language to control the IoT devices. AWS IoT Core manages IoT devices and their data. The IoT devices are registered with AWS IoT Core, and their unique identifiers are stored in the AWS IoT Core registry. The ESP32 module is programmed to connect to the wifi network using the wifi module. This enables the module to access the internet and connect to AWS IoT Core. Findings/Result: We developed a multiple-device controller using AWS IoT and ESP32 modules here. We created a single-channel IoT in AWS and broadcast it to all devices. Every message consists device id. All devices will receive the message but are responsible only for a specific device. For multicast messages, the response is by multiple devices. The c# application is the master; all ESP32 devices are clients. Originality/Value: Most net documents are dedicated to the IoT device creation procedure. Here we demonstrate the complete example, i.e., how to create IoT Devices in the AWS server, the node devices, and control from the C# application. So the interested researcher can get complete information to integrate IoT into their project. Paper Type: Experimental-based Research.

A Dynamic Step-wise Tiny Encryption Algorithm with Fruit Fly Optimization for Quality of Service improvement in healthcare

Healthcare highly depends on accessing, processing, analyzing, sharing, and storing electronic data. Cloud computing has evolved in various healthcare fields, such as bioinformatics, health management systems, and telemedicine. The Internet of Medical Things (IoMT), a subset of IoT, is an essential element of a healthcare management system. The expeditious development in IoMT, intelligent medical devices, healthcare applications, and high-speed networks has enabled medical systems to efficiently diagnose, treat, manage diseases, and remotely monitor and improve the Quality of Service (QoS). To achieve this high data, high speed network, longer battery life and reliable connectivity are basic requirements. 5G conformed these needs with its numerous and distinct potential. However, the shortcomings of these medical devices in the IoMT are privacy concerns and direct physical threats. We propose a new algorithm to enhance the QoS of the IoMT devices/systems. Initially, the dataset is collected and the 5G network is operationalized. We propose a Dynamic Step-wise Tiny Encryption Algorithm with Fruit Fly Optimization Algorithm to encrypt data. Encrypted data is transmitted using the Energy Efficient Routing Protocol. When the user requests to access the data, the data is decrypted. We show that the proposed system has higher QoS attributes and is more effective. Our experimental results show that our proposed solution considerably improves QoS and network performance in terms of lifetime, communication overhead, storage footprint, and power consumption compared to the competing methods.

Study of the semantic core of the text of online advertising in the hospitality industry

The article deals with the problems of search engine optimization in terms of the competent compilation of the semantic core of the resource, basically, its semantic content in order to determine what information should be conveyed to the user and in what way. The methods of compiling the semantic core can be divided into two groups. The first group is based on the assumption of what queries users can enter. The second group includes the methods based on evidence and the use of tools that allow seeing real user requests. Using a combination of these two methods, one can create a semantic core that will effectively promote hotel services and increase the effectiveness of advertising.

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.