Reviewerly/exHarmony huggingface dataset:
Ontology-aware Search and Analytics with Elasicsearch: Case study for Epidemiological Investigation

Inverted index is widely used for semantic search using ontology. Elasticsearch is inverted index-based open source search engine and has several advantages such as scalability. In this paper, we propose a method to achieve ontology-aware semantic search and analytics with Elasticsearch. Our proposed method is based on combination of semantic indexing and query expansion, and enables search and analytics by structured and keyword queries for both structured and text data. Experimental results for epidemiological investigation showed that proposed method has enough capabilities for practical semantic search, especially with semantic analytics function considering ontological relationships in near real-time.
Evaluation of AI Algorithms used for Non- Masked and Masked Face Recognition

Face recognition is one of the fundamental uses of picture assessment. Total 10 no. of various algorithms and technique are discussed in this paper out of which four are Non-masked face recognition and four are masked face recognition techniques. Comparisons are performed based on accuracy of all the algorithms. It is also discussed that it may be included in areas like Information Security, Access Management, Biometrics, Law Enforcements, Personal Security & Entertainment purposes as well. We use this paper as a medium to assess the performance of various existing state-of-the-art techniques and methods for masked as well as unmasked facial recognition. In the domain of masked facial recognition, it is a known fact that there is a dearth of relevant database available in the publicly accessible zone, even with the Even with the availability of limited dataset, our results depict that the exiting comprehensive techniques are able to deliver an accuracy of more than ninety percent. This paper aims to disseminate information with respect to the methodology followed in every technique and the accuracy level it has achieved.
An AI-Centred Adaptive Cognitive Network Traffic Modelling &amp; Measurement Technique For EIS

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis & transfer are crucial. For net management & traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads & precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity & take appropriate steps in response. In order to decrease network monitoring overheads & increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate & forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern & networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment & optimise the measured values, the matrices refilling & optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity.
Use of Block-chain and Internet of Things (IoT) for Ensuring Privacy and Reliability of Well Being Records in Medical Services

The medical services framework has key reliability and protection necessities when looked at like as a venture, for example, shielding patients' clinical accounts from undesirable access, safeguarded medication following, protected association with conveyance like ambulances, as well as secure and savvy e-wellbeing observation. Through reasonable safety efforts, distributed ledger technology provides novel ideas reliability and wellbeing of clinical details, and this might accommodate the error among distribution of details and classification. We consolidate qualities of both distributed ledger technology as well as distributed computation in this exploration to give a classification strategy to obstruct chain as well as IoT. This methodology consolidates IoT and conveys IoT administrations to hinder chain hubs; meanwhile, it assembles, analyses, works, and jam in the personality approval for wellbeing material. Cooperation and addresses the deficient registering abilities of some block tie hubs to affirm details legitimacy and possibility.The proposed approach is productive, as exhibited by the reproduction try. It can safeguard and check the honesty of clinical details while additionally resolving issues, for example, high PC intricacy, details trade, and reliability insurance.
Machine Learning for Privacy Preservation with Minimal Information Leakage

A wide range of application sectors are progressively using machine learning (ML). A successful machine learning (ML) model often requires a lot of training data and powerful processing resources, especially the newly developing deep neural network model. Due to the possibility of information leakage and the changing legislative landscapes that progressively limit Privacy-sensitive data access and use, the need for large amounts of available data, raises significant privacy concerns. Additionally, adversarial techniques. Therefore, carefully thought-out privacy-preserving machine learning (PPML) solutions are essential and are generating more and more research attention from academia and business. More and more PPML initiatives are being put forth through the incorporation of privacy-preserving methods into machine learning algorithms. As existing PPML techniques intersect with ML, systems, security, and privacy, among others, there is an important requirement to understand current research, associated barriers, and roadmaps for future research. This study offers a PGU model to help evaluate various PPML solutions by intricately breaking down each solution's privacy-preserving functions. It also evaluates and summarizes existing privacy-preserving methodologies in a methodical manner. Phase, Guarantee, and Technical Utility make up the PGU model's trinity. We also address the special features and difficulties of PPML and suggest future study avenues that will be beneficial to a wide variety of research communities in the fields of Machine learning, security systems, distributed systems and privacy system.
Recent Advances in DL-based Text Summarization: A Systematic Review

The technique of creating a brief and relevant summary of a lengthy piece of text while retaining its vital content and overall relevance is known as text summarization. It involves condensing the original text while retaining its core message. In today's age of information overload, text summarization has gained immense significance as we encounter an excessive amount of textual information on a daily basis. Text summarization can be done manually by humans, but it can also be automated using ML techniques. DL models have demonstrated promising results in text summarization in recent years, and have become a major study area in the field of NLP. This study offers a synopsis of literature on the use of DL approaches for text summarization. The review covers various techniques such as CNN, RNN, LSTM, DeepSum, GA2C, Pointer Generator, and BERT, as well as various datasets such as CNN/Daily Mail and Arabic datasets. The ROUGE Score was used to assess the efficacy of text summarizing approaches, and BERT received the highest score of 98%. This study gives a detail examination of the current advantage in DL approaches for text summarization. Furthermore, it indicates possible new study areas in this subject.
The Novel E-Way of Identifying the Face Mask and To Ware the System in the Crowd Management System

Since World War II, the Coronavirus has been the most critical charity test the world has faced. The globe has fought the epidemic with incredible bravery, and the early phases have been particularly heart-breaking. Correctly wearing a veil had shown to be an effective method of limiting the transmission of illness. We're working on developing a technique that will allow us to detect fine-grained veil wear issues. There are three primary obstacles to overcome in this project: 1) a variety of facial expressions, 2) a variety of veil types and instances; and 3) a variety of facial impediments We built a new, reasonable dataset of 4000 photos to figure out what was causing the issues. The proposed method is to leverage SSD with Caffe and MobileNetV2 engineering as a picture classifier to provide continuous veil recognition. The accuracy of this model is 96 percent. On this decent dataset, more high-level models for warm scanning and facial recognition are constructed.
Investigating Low &amp; High Transfers Training for Multi-task Face Identification Using a Semi-supervised Neuronal Networks

Recently years have seen a significant increase in research on visual identification issues including identification, aging, sexuality, & expression identification. This same classification of the majority of non-verbal Living beings Interactions scenarios became dependent on their implementation in robotics systems. Deep convolutional neuronal systems have demonstrated to be efficient in this respect when analyzing various face expressions, but at a heavy price: they need a sizable quantity of task-specific labelled information to attain maximal generalisation. A uniform semi-supervised deep network system is suggested in this research to deal with this issue. Our combination model consists of a set of permutation networks that fine-tunes the high-level faces notions for the identification of individuality, time of life team, sex, & facial expressions, as well as an unsupervised deep generative confrontational connection that gets to know basic character traits of face depictions.or the way you look. To avoid the need for a significant quantity of highly labelled learning information, our system uses continuous laterally interconnections among the convolutional streams so that it can exchange the high-abstraction specifics of all of such jobs. We suggest a number of tests to assess each component of our hybrids system, particularly the influence of the progressing interconnections on acquiring the individual image identification problems, & we find that our framework outperforms task-specific systems.
Smart City Sharing Economy Services Framework Based on Blockchain and Iot

In this study, we propose a Blockchain (BC) based architecture to allow spatio-temporal cryptographic protocol solutions for the long-term collaborative consumption that is allowed by the Internet of Things (IoT) in mega smart cities. These services are intended to be used in conjunction with mega smart cities. The architecture hosts and processes transactions from mobile edge and IoT nodes using cognitive fog nodes at the edge. Additionally, geo-tagged multimedia content is offloaded using this infrastructure. In addition to this, it utilizes AI to analyse and extract crucial event information, develop semantic digital analytics, and preserve the findings in BC and decentralized cloud repositories in order to promote sharing economy services. The framework offers a long-term incentive structure that may make it possible for secure smart city services such as the sharing economy, smart contracts, and cyber-physical interaction using BC and IoT to be implemented. Our unique contribution may now be recognized and appreciated thanks to the meticulous planning and execution of the system.
An Adaptive Service-Oriented Business Management Pattern Based on Machine Learning Rule ML

The significance of artificial intelligence (AI) and machine learning (ML) for businesses is briefly and comprehensively discussed in this study. AI is a new trend and technology of the modern era that offers numerous advantages to businesses. AI and machine learning save the company money by lowering over- all business operations costs. In addition, it enables businesses to effectively solve business issues and helps them make better decisions about their processes. Customers can communicate with the AI-based chat bots at any time, day or night, and they can answer their questions about any business or product. Based on business operations, ML creates opportunities for businesses and automates the process completely. In addition, the ML effectively enhances cognitive engagement between employees and customers and offers solutions to customer issues like password issues and many others. In addition, the study contains the strategies and procedures utilized in its completion. In this study, the researcher has used secondary qualitative and quantitative methods to collect data. Companies must comprehend the augmentation and automation process in order to implement AI and ML in business operations. In addition, the type of business is used to describe the Al’s market size in percentage and dollars.
Blockchain Technology for Secured Transactions in Banking

Customers who use internet banking have the flexibility to manage their assets sometimes when and wherever they choose. Any web-based transactions will, in any instance, be vulnerable to privacy risks. The current system makes use of two-way validation factors (OTP), which are easily cracked by cybercriminals, causing customers to have their personal information invaded without having access to the aggressor's recognised evidence. As a result, in this research, we suggest a flexible SIM sequential-based check approach to safeguard mobile transactions using a waiter-side secure platform built on the Blockchain. The customer's record has the supporter character module (sim) serial number, and if a gate crasher tries to initiate a transaction from another sim, there is a mix-up of the login credentials, at which point the system communicates the intrusive location information to the bank. The bank then sends an email to the subscribed customer alerting them to the peculiarities of the intruder. There is no risk of a gate crasher initiating the transaction from another device because the mobile sim chronicle number is unique to the sim and isn't literally very distanced, eliminating the concerns associated with OTP. By verifying the updated markings throughout the transaction and calculating the contract for transaction confirmation, the Ethereum Blockchain breakthrough provides server-side information base security. For reliable financial exchange, blockchain-based security is mathematically validated.
Implementation of Artificial Intelligence Technology for Better Irrigation System

Agriculture is a vital industry for economic growth, but inefficient irrigation techniques can waste a lot of water. Artificial intelligence (AI) technology integration in irrigation systems has been suggested as a viable remedy to this problem. This study describes a system that automates irrigation and only waters crops when necessary depending on climate and soil moisture conditions. The system incorporates data on soil moisture, AI, and the Internet of Things (IoT). During testing in a controlled environment, this integrated strategy demonstrated promising results with an accuracy rate of 80%. However, it is crucial to take into account how readily available and inexpensive such technologies are for all farmers, especially those in underdeveloped nations where the agricultural industry is dominated by informal labor. AI technology integration in irrigation systems can increase agricultural output and efficiency while lowering water waste. To make sure that such technologies are available and advantageous for all farmers, it is vital to take into account the socioeconomic environment and potential adoption barriers. The feasibility and scalability of deploying such systems on a bigger scale could be the subject of future research.
Abenet: An emotion recognition for Bengali language

, Ābēga (Known as Emotion in Bangla), a fundamental and significant part of who we are is shaped by our emotions. Though it may not always be obvious, everything we do, and say reflects some of our emotions in some way. Human behavior can be understood if we analyze these emotions from data. Text, speech, and facial expressions are all examples of this data. The challenge of quantifying emotions is highly difficult, and although significant progress has been made, there is still much room for improvement. Since social networking websites have become so popular, many individuals have developed a strong interest in exploring the material that is posted on these different websites. By studying this data across the Internet, we are traversing the whole continent, all of its cultures and communities. The work for regional languages is limited in this particular domain. So, the purpose of this paper is to propose a model which can accurately predict or classify emotions in regional languages.
Artificial Intelligence &amp; AGILE Based Business Development Review

In this essay, I want to look at how to provide products to customers as rapidly as possible without sacrificing product quality. We will examine how the six consistent design principles may assist us in ensuring that the finished product has the highest attainable quality in the shortest amount of time imaginable throughout the improvement interaction. Giving wonderful items quickly for what it's worth to accomplish as such successfully is equally important. There are several strategies we may employ to achieve this, such availability techniques, which enable us to retain the highest level of quality we are capable of as a software development team. Along with a few strategies for expediting product delivery and including a better driver software architecture in the driver software, he explains these strategies. A sizable portion of India's economy is focused on the food industry. The great majority of individuals in this area rely mostly on agricultural produce for their income, which benefits the whole community. The development of food, which is fundamental for human food, is heavily influenced by ranchers. The sales reps are attempting to decide the right selling cost for the offers they are giving. The misfortunes of this organization outperform its benefits. As indicated by the financial exchange hypothesis referenced above, ranchers are allowed to sell their wares at the most elevated offer cost. Ranchers that sign up in this manner will approach different administrations, including assessments, distributer correspondence, market warnings, and different elements and benefits of a comparable kind. Predictive analytics may be used in many other industries, such as expert device manufacture (which might include voice recognition and artificial vision), judicial assessment, and a number of others. Precision agriculture, automated weapons systems (such as drones), and judicial precedent analysis are a few examples. The adoption of artificial intelligence agricultural methods will help alleviate the challenge of the rising global food demand. Artificial intelligence would make it simple to identify and target weeds, and decisions on the use of pesticides and the appropriate buffer zone could be made quickly. It was chosen to include certain artificial intelligence (AI) components in this paper.
A Detailed Multi Regression Examination of the Precarious Role of AI in Ensured High-Performer CC Systems

The successful distribution of computer services, including software, storage needs, analytics, intelligence, and many others, is reflected in cloud computing. Additionally, it provides both quicker innovation and flexible resources. A CC system in an organization offers high levels of data protection at a minimal maintenance cost. Through cloud technologies, there has been a significant amount of industry collaboration. All of those processes have been improved thanks to the use of AI. The adoption of AI in those sectors has a significant impact on how effective CC methods are, and as a result, the combined effect of those two technologies increases the profitability of those businesses significantly. The performance of cloud systems is improved by the use of cognitive computing and ML models. Incorporating AI into CC strategies has also been shown to provide a number of advantages for businesses. This essay includes a thorough introduction and an insightful literature review. Three research questions are mentioned in a study methodology that has also been provided. The data analysis section includes aMRAusing years and percentages of AI usage in various firms as independent variables. The profitability variable is dependent. Additionally, a critical discussion section and a conclusion with a few suggestions have been included.
Employing Acoustic Properties to Authenticate Mobile Devices

The goal of this project is to use hardware components' built-in manufacturing faults as mobile phone IDs. We assessed the applicability of several I/O-related cell phone components, including sensors. Through this process, the focus was on creating hardware issue samples that could then be categorised using the device's speaker and microphone. In our technique, an audio sample was created by playing a known audio file via a mobile phone's speakers and then recording the sound using the same device. The impact of important variables on sample accuracy was examined using a variety of different sample groups. After collecting the samples, the frequency responses were extracted and classified. Data were categorised using a variety of classifiers, with certain label and sample group configurations achieving an accuracy of over 94.4%. The conclusions of this article suggest that speaker and mike production faults may be exploited for device authentication.
Accurate Face Annotations utilizing Deep CNN

In this abstract, we introduce the MTL-CNN, a multi-task learning-based multilayer neuronal net that can concurrently calculate multiple items for facial image. Age range, gender, & visual features make up the model's overall capacity to assess up to 74 different appearance characteristics from 3 distinct detection tests . The suggested framework introduces 2 parts: I a gating method to govern activation distribution & to flexibly route these among various appearance qualities; and (ii) a modules to comment the forecasts in addition to taking into consideration the connection between face qualities. This model distributes all the CNN's characteristics between assignments and handles with undertaking estimates. The model is developed by combining different database to increase the amount of facial qualities that can be evaluated, and by utilizing a center loss to unify presentations of different facial characteristics inside the embed region. Numerous tests confirm that the suggested strategy works.
The Role of AI and Blockchain in Supply Chain Traceability

Optimizing the abilities to boost numerous goals, such as budget, service, and reliability, is at the core of industrial automation. To do this, supply chain management must adopt the product lifecycle, manufacturing systems lifecycle, and economic cycle. In a nutshell, a manufacturing company generates a massive quantity of data. A distribution network stakeholder has accessibility to its data and may utilize AI to draw new insights. Since only little information is provided to the consumer, only a limited context can be established. With blockchain, everyone can see everyone else's data. As a result, an individual's stakeholder insights will be more substantial. This will aid in establishing an intelligent supply chain