Domain-Independent Natural Language Processing of text using Unsupervised Translation

NLP is one of the very important domains of artificial intelligence. Nowadays, advancements are being made and NLP is one of the most developing fields. In this paper, we offer a mutual use of unsupervised translation with n-grams and Natural Language Processing techniques to challenge the difficulty of unsupervised translation extraction from textual data. To build a Text Meaning Extraction System, we have to deliver one important element which is input text. This studypresented a different algorithm to work out resemblances between natural languages, by using sequence package analysis and changing them into n-grams. Whenever the sentences that are grammatically difficult and quite lengthy are applied to see the results of the presented algorithm, there are quite efficient results in a semantic reaction. To enhance the experience in the field of AI and search engines, this research paper shows how to improve the handling capability of fuzzy concepts within computers. For example, when search jobs are executed in search engines small textual concepts or sentences might be semantically formed to switch the keyword-based queries. This ability may be functional to intelligent agents to even the procedure of communication between humans and machinery

Cloud Computing: Needs Enabling Data Mining and Business Intelligent Applications

As a new computational paradigm, cloud computing is attracting a lot of interest from researchers in the field of the business community and information technology sector and it can integrate with several heterogeneous resources makes it distinguished and unique to fulfill the demands of different types of users. The rapid increase in data volume and fixed access to online resources, which is related to all departments need to mine data for the discovery of knowledge.  Its principal peculiarities incorporate a versatile asset design and along this line a suitable system for tending to be comprehended in an ideal mode. From the specific situations where cloud computing can be integrated, its use in business information and intelligence also conveys the highest aspirations from data mining to updates. This study gives an outline of the recent condition of the arrangement of Cloud Computing and elaborates, its implications in Business Intelligence and Data Mining. This study defines multiple layers that are expected to create such a framework in distinctive levels of deliberation, from the fundamental equipment stages to the product assets accessible to actualize the applications. At the end of this study, a few cases related to Data mining methodologies have been relocated to the Cloud Computing paradigm

IoT based Smart Traffic Signal: Time Stealer

The increased numbers of private vehicles on the roads has added much to the traffic congestions and wait time at the intersections. To make cities smart and smart cities smarter by reducing this problem, the number of systems has been and is being developed, as it also causes the wastage of time, fuel and also increases air and noise pollution. In this paper, we present a system ‘Time Stealer’ which will check the density of the traffic on the sides of the road and will give green light time accordingly keeping in view that all sides get their turn. This system will reduce the wait time that vehicles do even when there is no traffic on the green side and will also avoid signal jumping which is very common in our country and also a cause of the number of accident

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Abstract The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared ( $$R^2$$ R 2 ). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available

Design and Development of AI-Based Tourist Facilitator and Information Agent

Due to the rapid increase in the demand for information that supports tourists after, before, and during the trip, many tour systems are available. However, these systems are not able to successfully replace a human facilitator that is expensive to hire. The primary key qualities of a human tourist guide are his/her knowledge, communication skills, and interpretation of destination attractions. Traditional tourist facilitator systems are lacking in these qualities. The main idea of the research is to design an agent to guide tourists, provide them accurate information about visitable places, without having any bound for a specific region and it will have human-like communication skills along with the point of interest knowledge, which depends on its internal knowledge base as well as its online searching techniques

Prioritization of Exigency Services in Multi-Agent Transportation Systems

Multi-agent system helps in achieving a single global goal by working on different tasks in a distributed environment. This research presents a new framework for handling the prioritization of exigency services in an urban transportation system. Prioritization in exigency services needs to be handled for vehicles like Ambulance, Police Mobile, Fire Brigade, Bomb Disposal Squads, Search and Rescue vehicles, Turntable Ladder, and other similar vehicles. In the proposed framework a single ARTIS agent with four In-agents is deployed at each signal node. In-Agents at different nodes share information about the exigency with the ARTIS Agent that analyses the input data and determines the types of conflict that might cause delays in emergency services provision. We operate the signal based on their priorities for exigency vehicles with different priorities. In case they have the same priorities then we use the lane congestion and vehicle wait time to operate a signal. We demonstrate the application of our proposed approach using different cases of a traffic case study.

Surface modified carbon nanotubes fiber as flexible bifunctional electrocatalyst for overall electrochemical water splitting reactions

Electrocatalytic water splitting is regarded as a promising approach to produce hydrogen, which is a clean and renewable fuel. The process is mainly constrained due to the sluggish proton-coupled four-electrode transfer process at the anode for oxygen evolution reaction (OER) with high overpotential requirement. Herein this work, we used a one-step hydrothermal method for the in-situ synthesis of CoSe nanoparticles over the surface of carbon nanotube-based fiber (CNTs fiber) and utilized it as a bifunctional electrocatalyst for the electrochemical water splitting process. Surface-modified fiber showed excellent performance towards OER with a low overpotential (η10 = 414 mV) and Tafel slope (77 mVdec−1). We also exploited the same material as cathode, which exhibited an excellent hydrogen evolution reaction (HER) at the counterpart with improved catalytic performance as compared to bare CNTs materials. During the HER process in the cathodic potential region, the electrocatalyst displayed a current density of 10 mAcm−2 at an overpotential of 496 mV. Furthermore, the electrocatalyst exhibited excellent performance during the testing for the overall water splitting. The outcomes reveal that the fabricated electrode can be potentially applied as an efficient and flexible electrode to derive the hydrogen as fuels during the overall electrochemical water splitting reaction

Microstructure and Corrosion Behavior of Atmospheric Plasma Sprayed NiCoCrAlFe High Entropy Alloy Coating

High entropy alloys (HEAs) are multi-elemental alloy systems that exhibit a combination of exceptional mechanical and physical properties, and nowadays are validating their potential in the form of thermal sprayed coatings. In the present study, a novel synthesis method is presented to form high entropy alloy coatings. For this purpose, thermal sprayed coatings were deposited on Stainless Steel 316L substrates using atmospheric plasma spraying technique with subsequent annealing, at 1000 °C for 4 h, to assist alloy formation by thermal diffusion. The coatings in as-coated samples as well as in annealed forms were extensively studied by SEM for microstructure and cross-sectional analysis. Phase identification was performed by X-ray diffraction studies. The annealed coatings revealed a mixed BCC and FCC based HEA structure. Potentiodynamic corrosion behavior of SS316L sprayed as well as annealed coatings were also carried out in 3.5% NaCl solution and it was found that the HEA-based annealed coatings displayed the best corrosion resistance 0.83 (mpy), as compared to coated/non-annealed and SS 316 L that showed corrosion resistance of 7.60 (mpy) and 3.04 (mpy), respectively

Microwave assisted extraction and dyeing of cotton fabric with mixed natural dye from pomegranate rind (Punica granatum L.) and turmeric rhizome (Curcuma longa L.)

Recently, natural dyes are used because these are environment-friendly, less lethal, and do not have any detrimental effect on health. For the present study, the cotton fabric and the mixed powder (pomegranate rind and turmeric rhizome) have been irradiated for (1–5) min. It has been found that 3 min is the effective exposure time for improvement in dyeing behavior of cellulosic fabric. Good color strength was observed by dyeing fabric irradiated at 65°C for 40 min in dyeing bath having pH 6. For improvements in color fastness, the optimum concentration of pre-mordant (4% copper) and post-mordant (8% chrome) was employed. It is observed that microwaves increased the color strength as well as color fastness properties of irradiated cotton using aqueous solubilized mixed extract of irradiated pomegranate rind and turmeric rhizome powder