Showing papers in "International Journal For Science Technology And Engineering in 2023"
TL;DR: In this article , the authors proposed a bidirectional DC/DC converter that interfaces a main energy storage, an auxiliary energy storage and a DC-bus of different voltage levels, for application in hybrid electric vehicle systems.
Abstract: Abstract: This study develops a newly designed, patented, bidirectional DC/DC converter (BDC) that interfaces a main energy storage (ES1), an auxiliary energy storage (ES2), and a DC-bus of different voltage levels, for application in hybrid electric vehicle systems. The proposed converter can operate in a step-up mode (i.e., low-voltage dual-source-powering mode) and a stepdown (i.e., high-voltage dc-link energy-regenerating mode), both with bidirectional power flow control. In addition, the model can independently control power flow between any two low-voltage sources (i.e., low-voltage dual-source buck/boost mode). Herein, the circuit configuration, operation, steady-state analysis, and closed-loop control of the proposed BDC are discussed according to its three modes of power transfer.
34 citations
TL;DR: In this article , the most useful models and important criteria for predicting home values are examined in a literature review, and the adoption of Random Forest and XGBoost as the most effective models in comparison to others was confirmed by this study's findings.
Abstract: Abstract: The real estate industry is seeing an increase in the use of data mining. The capacity of data mining to extricate helpful data from crude information makes it especially helpful for anticipating home estimations, essential housing characteristics, and a great many different elements. Homeowners and the real estate industry frequently feel anxious about price swings, according to research. The most useful models and important criteria for predicting home values are examined in a literature review. The adoption of Random Forest and XGBoost as the most effective models in comparison to others was confirmed by this study's findings. Additionally, our data suggest that locational and structural characteristics are significant forecasting variables for housing values. In order to identify the most effective machine learning model for conducting a study in this field and the most significant factors that influence home prices, this study will be very helpful, particularly to housing developers and academics.
8 citations
TL;DR: In this paper , a system is introduced to manage waste in big cities effectively without having tomonitor the parts 24x7 manually. But the problem of unorganized and non- systematic waste collection is solved by designing an embedded IoT system which will monitor each dumpster individually for the amount of waste deposited.
Abstract: Abstract: In this paper, a system is introduced to manage waste in big cities effectively without having tomonitor the parts 24x7 manually. Here the problemof unorganized and non- systematic wastecollection is solved by designing an embedded IoT system which will monitor each dumpster individually for the amount of waste deposited. Here an automated system is provided for segregating wet and dry waste. A mechanical setup can be used for separating wet and dry waste into separate containers here sensors can be used for separating wet and dry. For detecting the presence of any waste wet or dry can be detected using anIR sensor in the next step for detecting wet waste a moister sensor can be used. In this process, if only IR is detected motor will rotate in the direction of the dry waste container if both the sensor detectsthe waste then it will go to the wet container. Both these containers are embedded with ultrasonic sensors at the top, the ultrasonic sensor is used for measuring distance. This makes it possible to measure the amount of waste in the containers if one of the containers is full then alert message will be sent to the corresponding personal.
8 citations
TL;DR: This research proposes a robust detection mechanism that can deal with variation in age, illumination, eye and head gears, and a deep learning based feature extractor along with a classifier is adopted to augment the detection results.
Abstract: Failure of facial recognition and authentication system may lead to several unlawful activities. The current facial recognition systems are vulnerable to different biometric attacks. This research focuses on morphing attack detection. This research proposes a robust detection mechanism that can deal with variation in age, illumination, eye and head gears. A deep learning based feature extractor along with a classifier is adopted. Additionally, image enhancement and feature combination are proposed to augment the detection results. A versatile dataset is also developed that contains Morph-2 and Morph-3 images, created by sophisticated tools with manual intervention. Morph-3 images can give more realistic appearance and hence difficult to detect. Moreover, Morph-3 images are not considered in the literature before. Professional morphing software depicts more realistic morph attack scenario as compared to the morphs generated in the previous work from free programs and code scripts. Eight face databases are used for creation of morphs to encompass the variation. These databases are Celebrity2000, Extended Yale, FEI, FGNET, GT-DB, MULTI-PIE, FERET and FRLL. Results are investigated using multiple experimental setups and it is concluded that the proposed methodology gives promising results.
6 citations
TL;DR: The authors proposed Universal Language Model Fine-tuning (ULMFiT), an efficient transfer learning method that can be used for any NLP activity, such as text categorization.
Abstract: Abstract: We describe approaches that are essential for fine-tuning a language model further utilized for text categorization and propose Universal Language Model Fine-tuning (ULMFiT), an efficient transfer learning method that can be used for any NLP activity. Transfer learning methods have greatly impacted computer vision, but existing approaches in NLP still require taskspecific modifications. Universal Language Model Fine-tuning, or ULMFiT, is an architecture and transfer learning method that can be applied to NLP tasks. It involves a 3-layer architecture. Three steps make up the training process: pre-training for the general language model on a text taken from Wikipedia; fine-tuning the language model on a target task; and fine-tuning the classifier on the target task. Deep learning techniques enable computers to learn and comprehend natural language, facilitating human-machine interaction. Deep learning models are often employed in medical research, from medication candidate identification to picture analysis. On text classification tasks, our method greatly surpasses the state-of-the-art (it is the most recent model incorporating the best and latest technology), reducing the error on most datasets. Furthermore, with only a few labeled examples, it can match the performanceof training on 100× more data.
5 citations
TL;DR: In this paper , the authors used blockchain technology for the identification of actual merchandise and the detecting of faux products, which can be used to store product info and generate particular code of that product as blocks inside the database.
Abstract: Abstract: There are several fake products in the existing supply chain markets. It is necessary to have a system for end users to check all details about the product they are buying so that the customer can check if the product is genuine. In recent years, faking products play an important role in product manufacturing industries. Faking products affects the company name, sales, and profit of the organizations. Blockchain technology is used for the identification of actual merchandise and the detecting of faux products. Blockchain technology is generally a tally system that stores all the data of the deals that take place in it. The precise aspect approximately this era is that the tally we referred to is a allotted tally in a peer-to- peer community. Blockchain generation is relaxed as the facts saved once within the chain is immutable consequently any block cannot be modified or hacked. with the aid of using Blockchain era, clients or customers do no longer want to rely on users for confirmation of product authenticity and protection. Our device affords the rising generation of web use instances, and brief reaction (QR) codes offer a unique but affectable method to triumph over these faking of products. Counterfeited merchandise may be detected the use of a QR code scanner, wherein the QR code of the product is linked to the Blockchain. So, this system can be used to store product info and generate particular code of that product as blocks inside the database. It collects the precise code from the consumer and compares the code against entities within the Blockchain database. If the code matches, it will give all the information about the product otherwise no data can be outputted to the purchaser which shows that the product is fake or counterfeited.
4 citations
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TL;DR: In this article , the authors examined the Rain Water Harvesting (RWH) system as a substitute for the BBDITM H-block as a source of water, which satisfies social requirements and maybe implemented in both urban and rural areas.
Abstract: Abstract: One of the severe issues that is well recognized on the planet is the water scarcity. Overexploitation of groundwater and surface water resources is the outcome of population growth, urbanization, and industrial expansion. Due to uneven rainfall, the traditional water sources, such as wells, rivers, and reservoirs, are unable to supply all of the water needed .While a new water source is being investigated by the rainwater gathering system. Utilizing rainwater is the study goal, which is closely related to the idea of protecting the environment. This study examines the Rain Water Harvesting (RWH) system as a substitute for the BBDITM H-block as a source of water. By taking into account nearly all technological aspects, the development system satisfies social requirements and maybe implemented in both urban & rural areas.
4 citations
TL;DR: In this paper , a pure convolutional neural network approach outperformed the results of other statistical methods obtained by other authors, including feature engineering, for facial expression recognition. But the results achieved did not correspond to the state of the art.
Abstract: Abstract: The use of machines to perform various tasks is ever increasing in society. By imbuing machines with perception, they will be able to perform a wide variety of tasks. There are also very complex ones, such as aged care. Machine perception requires the machine to understand the surrounding environment and the intentions of the interlocutor. Recognizing facial emotions can help in this regard. During the development of this work, deep learning techniques were used on images showing facial emotions such as happiness, sadness, anger, surprise, disgust, and fear. In this study, a pure convolutional neural network approach outperformed the results of other statistical methods obtained by other authors, including feature engineering. The use of convolutional networks includes a learning function. This looks very promising for this task where the functionality is not easy to define. Additionally, the network he was evaluated using two different corpora. One was used during network training and also helped tune parameters and define the network architecture. This corpus consisted of mimetic emotions. The network that yielded the highest classification accuracy results was tested on the second dataset. Although the network was trained on only one corpus, the network reported promising results when tested on another dataset showing non-real facial emotions. The results achieved did not correspond to the state of the art. Collected evidence indicates that deep learning may be suitable for facial expression classification. Deep learning therefore has the potential to improve human-machine interaction. Because the ability to learn functions allows machines to evolve cognition. And through perception, the machine could offer a smoother response, greatly improving the user's experience.
4 citations
TL;DR: In this article , the authors developed a project on the concept that is "SMART TROLLEY" which is totally automatic in order to avoid the hustle-like pushing trolley, waiting in billing queue, thinking about budget.
Abstract: bstract: Nowadays, in mall for purchasing variety of items it requires trolley. Every time customer must pull the trolley from aisle to aisle for collecting items and at the same time customer has to do calculation of those items and need to compare it with his budget in pocket. After this procedure, customer must wait in queue for billing. So, to avoid the hustlelike pushing trolley, waiting in billing queue, thinking about budget, we have developed a project on the concept that is “SMART TROLLEY”. In today’s world, for automation of mall we are developing an IoT based TROLLEY which is totally automatic. It helpsthe customer while purchasing items.Customer must place the product inside and scan RFID tags ofthe product, RFID Reader will detect the tag and fetch the product details from the local database. The corresponding dataregarding the product will be then displayed on LCD screen display which is mounted on the trolley. When the customer issatisfied with the shopping, they have to press a button which will wirelessly send the billdetails to the billing counter. By using this trolley, customer can buy large number of products in very less time with minimum efforts. At the billing counter, computer can be easily interfaced for verification and bill printout.
3 citations
TL;DR: In this paper , the authors used machine learning techniques to identify patients with major diseases like Heart disease, Kidney disease and Diabetes disease at early stage so that proper treatments can be given to them.
Abstract: Abstract: There are number of hospitals in the world with advanced diagnostic equipment. But although having this equipment some patients cannot get proper treatments and may suffer to death. Main reason behind this is time, our medical systems lack time and it is not easy for them to manage time. With help of machine learning techniques we created project that identifies patients with major diseases like Heart disease, Kidney disease and Diabetes disease at early stage so that proper treatments can be given to them. We collected three datasets for three models from Kaggle [1], analyzed[2]them, cleaned them and choose best algorithm [3] for each dataset. We achieved 98.52% accuracy on heart disease prediction model [4], 98.73% accuracy on kidney disease prediction model [5], 80.55% accuracy on diabetes disease prediction model [6]. For all three models we used Random Forest algorithm [7] .At the end we created web application using Flask [8] for easy user interaction.
2 citations
TL;DR: In this paper , a transformer defect diagnostic approach based on an Internet of Things (IoT) monitoring sysem and ensemble machine learning (EML) is presented, which is made up of deep belief networks (DBNs), stacked denoising auto encoders (SDAs) with distinct activation functions, and relevance vector machines (RVMs).
Abstract: Abstract: Transformers are critical components of electric powersystems, yet precise fault identificationremains difficult. The study presents a novel transformer defect diagnostic approach based on an Internet of Things (IoT) monitoring sysem and ensemble machine learning (EML). The IoTbased monitoring system is divided into two parts: data measuring subsystemand a data reception subsystem. To begin, the data measuring subsystem measures transformer vibration signals, which are then relayed to the remote server via the data receipt subsystem. Then an EML is proposed that is made up of deep belief networks (DBNs), stacked denoising auto encoders (SDAs) with distinct activation functions, and relevance vector machines (RVMs). DBN sand SDAs are respectively used to extract features from the signals, and RVMs are respectively employed as classifier. In order to ensure efficient of the EML, a novel combination strategy is proposed. A transformer fault diagnosis experiment is performed
TL;DR: In this paper , the authors proposed a novel algorithm using characteristics of speckle noise and filtering methods based on Speckle Reducing Anisotropic Diffusion (SRAD) filtering, Discrete Wavelet Transform (DWT), Weighted Guided Image Filtering (WGIF), and Gradient Domain-Guided Image Filter (GDGIF).
Abstract: Abstract: Ultrasound Imaging is used to examine various organs in the Human Body. However, in the process of obtaining a Ultrasound Image, Speckle noise is generated due to backscattered echo signals. So, a noise reduction method is required. To improve the speckle noise reduction, we propose a novel algorithm using characteristics of speckle noise and filtering methods based on Speckle Reducing Anisotropic Diffusion (SRAD) filtering, Discrete Wavelet Transform (DWT), Weighted Guided Image Filtering (WGIF) and Gradient Domain Guided Image Filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency subband images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform
TL;DR: In this article , the concept of which customer segment to target is done using the customer segmentation process using the clustering technique and elbow method is used to determine the optimal clusters, elbow methodis used.
Abstract: Abstract: We live in a world where large and vast amount of data is collected daily. Analysing such data is an important need. In the modern era of innovation, where there is a large competition to be better then everyone, the business strategy needs to be according to the modern conditions. The business done today runs on the basis of innovative ideas as there are large number of potential customers who are confounded to what to buy and what not to buy. The companies doing the business are also not able to diagnose the target potential customers. This is where the machine learning comes into picture, the various algorithms are applied to identify the hidden patterns in the data for better decision making. The concept of which customer segment to target is done using the customer segmentation process using the clustering technique. In this paper, the clustering algorithm used is Kmeans algorithm which is the partitioning algorithm, to segment the customers according to the similar characteristics. To determine the optimal clusters, elbow methodis used.
TL;DR: In this paper , a model for automatic diagnosis of 14 different diseases based on chest radiographs using machine learning algorithms was proposed. But deep learning models usually suffer from a lack of explain ability.
Abstract: Abstract: Automatic recognition of key chest X-ray results to help radiologists with clinical workflow tasks like time-sensitive triage, pneumothorax (CXR) case screening and unanticipated discoveries. Deep learning models have become a promising prediction technique with near human accuracy, but usually suffer from a lack of explain ability. Medical professionals can treat and diagnose illnesses more precisely using automated picture segmentation and feature analysis. In this paper, we propose a model for automatic diagnosis of 14 different diseases based on chest radiographs using machine learning algorithms. Chest X-rays offer a non-invasive (perhaps bedside) method for tracking the course of illness. A severity score prediction model for COVID-19 pneumonia on chest radiography is presented in this study.
TL;DR: In this paper , the theory underlying the manual analysis and design of an overhead circular water tank using the Working Stress Method (WSM) and software modeling and analysis using Stat-Pro is described.
Abstract: Abstract: Water tanks are frequently used for storing potable water. Due to inadequacy of water around the world, significance is given more on the water storage project. So water storage is very essential as it plays a vital role in everyday life. water tanks and Storage reservoirs are used to store water, petroleum products, liquid petroleum, and similar liquids. All tanks are analysed and designed as crack free structures to get rid off any leakage. This project provides a brief explanation of the theory underlying the manual analysis and design of an overhead circular water tank using the Working Stress Method (WSM) and software modeling and analysis using Stat-Pro.
TL;DR: In this article , the authors designed a model to detect the fraud activity in credit card transactions, which can provide most of the important features required to detect illegal and illicit transactions, but it is difficult to track the behavior and pattern of criminal transactions.
Abstract: Abstract: This paper is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed paper we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions.
TL;DR: In this article , a disease prediction based on symptoms using machine learning is proposed, where machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, and KNN are used to forecast the disease on the provided dataset.
Abstract: Abstract: Healthcare is a sector that is always changing. Healthcare professionals may find it challenging to stay current with the constant development of new technologies and treatments. As a result, the purpose of this research paper is to try and implement machine learning features in a specific system for health facilities. Knowing if we are ill at an early stage rather than finding out later is crucial. The entire process of treatment can be made much more effective if the disease is predicted ahead using specific machine learning algorithms as opposed to directly treating the patient. In this work, disease is predicted based on symptoms using machine learning. Machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, and KNN are used to forecast the disease on the provided dataset. As you can see, there are numerous potential applications of machine learning in clinical care in the areas of patient data improvement, diagnosis, and treatment, cost reduction, and improved patient safety.
TL;DR: In this article , the authors present the results of a survey on the health and safety of secondary school teachers in the area, with an emphasis on the Mbooni West district.
Abstract: Abstract: Every human endeavor has a reasonable reason to be concerned about health and safety. For the safety of the teaching staff to be ensured in schools, the equipment already in place must be properly maintained, and any missing pieces must be installed in accordance with health and safety regulations. This article presents the results of a survey on the health and safety of secondary school teachers in the area, with an emphasis on the Mbooni West district. Many secondary school administrators do not take the teaching staff's suggestions for policies and procedures to reduce safety threats into account. The teaching staff finds it challenging to assume responsibility for their own safety as a result. Thus, the study aimed to determine instructors' perceptions on their responsibility in protecting workplace health and safety. All teachers and assistant principals employed by the Teachers Service Commission (TSC) and the Secondary Schools Board of Management were the focus of the investigation (BOM). Although survey principles were a goal of the study, they weren't accessible at the time of data collection. The descriptive research design was used for this investigation. Data was collected using a questionnaire guide, and version 20 of the Statistical Package for Social Science (SPSS) was used for analysis. For data display, frequency tables and charts were used. The results showed that the majority of the teaching staff did not participate in training programmers that would have given them occupational safety skills. The majority of them did not participate in discussions about workplace safety regulations. This seriously compromised the safety of instructors at work, impairing their readiness to deal with health dangers and, consequently, their overall performance. In order to integrate teachers' safety policies with the institutions strategic plans for workplace health and safety, it is advised that the Ministry of Education, Science, and Technology coordinate training programmers for the teaching staff with the school administrations.
TL;DR: In this paper , a real-time Telegram messaging platform was used to detect missing persons using surveillance cameras, and the potential of AI, particularly in facial recognition, to improve the accuracy and speed of identifying and locating missing individuals, thereby aiding in search and rescue efforts.
Abstract: Abstract: The paper focuses on leveraging the power of Artificial Intelligence (AI) algorithms, specifically Convolutional Neural Networks (CNN) for image classification, along with the Python programming language and the Telegram messaging platform for real-time notifications. The objective is to enhance the efficiency and effectiveness of search and rescue operations by utilizing AI techniques to detect missing persons using surveillance cameras. This research aims to harness the potential of AI, particularly in facial recognition, to improve the accuracy and speed of identifying and locating missing individuals, thereby aiding in search and rescue efforts..
TL;DR: The Restaurant Management System will provide the user a new experience in the restaurant as mentioned in this paper , it will allow a manager and an owner to store the data and transactions made by customer, the system will allow user to order the food by scanning QR code placed on the table.
Abstract: Abstract: The Restaurant Management System will provide the user a new experience in the restaurant. It will allow a manager and an owner to store the data and transactions made by customer. The System will allow user to order the food by scanning QR code placed on the table. It will manage the employee master and stock details and also produce the bill receipt a per the customer convenience(i.e. via Whatsapp, Email, SMS). It will manage the workload of chefs also. That will be very beneficial for the restaurant to give more better service to their customer.
TL;DR: The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed.
Abstract: Abstract: The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. In this paper, we present the concept for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.
TL;DR: In this article , a water system framework that is robotized by utilizing controllable boundary soil dampness on the grounds that they are the significant elements to be controlled in Dad (Accuracy Horticulture).
Abstract: Abstract: IoT and Remote Detecting Innovation are generally utilized wherever in the current logical world. As the innovation is developing and evolving quickly, Remote detecting Organization (WSN) serves to update the innovation. In the examination field of remote sensor networks, the power effective time is a significant issue. The answer to this issue can be tackled by utilizing the LoRaWAN innovation with IoT. The fundamental thought of this is to grasp how information goes through a remote medium transmission utilizing remote sensor organization and checking framework. This paper plans a water system framework that is robotized by utilizing controllable boundary soil dampness on the grounds that they are the significant elements to be controlled in Dad (Accuracy Horticulture).
TL;DR: The idea that SL is a highly ordered and primarily symbolic collection of human gestures is what led to the development of universal gesture-based human-computer interaction (HCI) systems as mentioned in this paper .
Abstract: Abstract: Body language is one of the nonverbal methodsof communication, and it comprises hand gestures, arm movements, posturing, and gestures and facial expressions. One way to communicate information through the movement of the body is through gestures. HGR is a smart, intuitive, and easy method of human-computer interaction (HCI). HGR systems have two key applications: SLR and GBC. To help the deaf communicate with the hearing community, SLR tries to automaticallyinterpret SLs via a computer. The idea that SL is a highly ordered and primarily symbolic collection of human gestures is what led to the development of universal gesture-based HCI.
TL;DR: In this paper , an exhaustive review has been carried out for different aspects of non-destructive testing (NDT) adopted for RCC structures, with the aim of identifying those, which are practical for detecting defects at early in the production sequence as possible.
Abstract: Abstract: To properly maintain our public infrastructure, engineers and designers must learn different methods of inspection. An exhaustive review has been carried out for different aspects of non- destructive testing (NDT) adopted for RCC structures. NDT evaluates the remaining operation life of different components of structure. It provides an accurate diagnosis which allows prediction of extended life operation beyond the designed life. Different aspects are considered which includes condition assessment, durability, corrosion, condition ranking and service life of structures. In this review, several non-destructive inspection methods are evaluated, with the aim of identifying those, which are practical for detecting defects at early in the production sequence as possible. The methods used for carrying out non destructive analysis used by different investigators are also discussed. Merits and demerits of each method are also stated. RCC structures considered are reinforced buildings, bridges, ESRs, recently developed NDT techniques which are useful for prediction of performance of structure are also included.
TL;DR: In this article , a control strategy for the management of power flows with solar and wind energy sources in DC micro grid is discussed, where a dedicated converter should be used to maintain the voltage of the DC connection.
Abstract: Abstract: DC loads have proliferated rapidly on the market today and DC micro grids with renewable energies are being built as a potential solution to meet the rising demand for electricity. As different energy sources such as solar, wind, fuel cell, and diesel generators can be incorporated into the DC grid, it is important to control the power flow between the sources. An attempt is made in this paper to study the hybrid system consisting a three energy sources, namely wind energy, photovoltaic power source and Battery. Each of the three energy sources is controlled so as to deliver uninterrupted power supply to the load. A control strategy for the management of power flows with solar and wind energy sources in DC micro grid are discussed. Given that voltage profile regulation is critical in a standalone system, a dedicated converter should be used to maintain the voltage of the DC connection. The battery circuit regulates DC charging voltage, while the full power is derived from Solar and Wind to power the attached DC bus charges. An algorithm is developed to manage power flow between three outlets. The algorithm is evaluated in MATLAB / SIMULINK environments for different charging conditions and variations in solar and wind energy.
TL;DR: In this paper , the authors used various techniques like Ridge Regression and Classifier to predict the right crop using parameters like district, rainfall, temperature, area, a crop which would help the farmer to predict a crop yield prior to making the decision to make the final crop selection.
Abstract: Abstract: Agroecology is one of the oldest and noblest professions in India. Farmers face a lot of hardships while using traditional methods of farming in today's technologically centric world. Precision farming is a modern approach in comparison to traditional cultivation techniques. We are predicting the right crop using parameters like district, rainfall, temperature, area, a crop which would help the farmer to predict the crop yield prior to making the decision to cultivate their final crop. This method can provide the farmer with valuable insights and assist them. In this paper we are using various techniques like Ridge Regression and Classifier. We have used different datasets on these models to get a better accuracy
TL;DR: In this paper , a Custom blockchain-based approach leveraging smart contracts and decentralized off-chain storage for efficient product traceability in the healthcare supply chain is presented, where the smart contract guarantees data provenance, eliminates the need for intermediaries and provides a secure, immutable history of transactions to all stakeholders.
Abstract: Abstract: The blockchain typically described as a decentralized system in which transactional or ancient statistics are recorded, stored, and maintained throughout a peer-to-peer community of personal computers referred to as nodes. Counterfeit drugs are one consequence of such limitations within existing supply chains, which not only has serious adverse impact on human health but also causes severe economic loss to the healthcare industry. Blockchain technology has gained tremendous attention, with an escalating hobby in a plethora of several applications like safe and relaxed healthcare records management. Similarly, blockchain is reforming the traditional healthcare practices to an extra reliable means, in phrases of powerful prognosis and treatment through safe and cosy facts sharing using SHA Hash Generation Algorithm. Within the future, blockchain will be an era that can probably assist in personalized, authentic, and at ease healthcare by means of merging the entire actual-time scientific information of a patient’s fitness and offering it in an up to date cosy healthcare setup. In this paper, we evaluation each the present and modern day trends inside the subject of healthcare with the aid of imposing blockchain as a model. We also talk the packages of blockchain, at the side of the demanding situations confronted and destiny views. The proposed system executed blockchain implementation in distributed computing surroundings and it gives the automated restoration of invalid chain by using Consensus and Mining Algorithm. In this system, we present a Custom blockchain-based approach leveraging smart contracts and decentralized off-chain storage for efficient product traceability in the healthcare supply chain. The smart contract guarantees data provenance, eliminates the need for intermediaries and provides a secure, immutable history of transactions to all stakeholders. We present the system architecture and detailed algorithms that govern the working principles of our proposed solution. We perform testing and validation, and present cost and security analysis of the system to evaluate its effectiveness to enhance traceability within pharmaceutical supply chains.
TL;DR: In this article , the development of a smart cane with a distance measurement system is the primary goal of the project, and an ultrasonic sensor serves as the system's input, and earphones serve as its output.
Abstract: Abstract: Risky situations arise when visually challenged people attempt to identify the items in front of them while crossing the roadway. The development of a smart cane with a distance measurement system is the primary goal of this project. An ultrasonic sensor serves as the system's input, and earphones serve as its output. Additionally, the device features a GPS live finding system and a blind person's navigation guidance. Through the audio output, this device will warn blind persons of any obstructions so they can proceed safely and without incident. Blindness is a condition in which there is a loss of seeing due to physiological or neurological reasons. Total blindness is the complete absence of visual light perception, while partial blindness is the lack of integration within the development of the nervus opticus or visual centre of attention.
TL;DR: In this article , the cost effectiveness of pre-cast concrete construction of a small-scale building was investigated using face-to-face interviews conducted at the National Engineering Research and Development (NERD) Centre and a few pre-stressed concrete yards.
Abstract: Abstract: Building a house is one of the basic needs of humans. To build a house with high durability and low cost, engineer A.N.S. Kulasinghe introduced the pre-cast concrete technology in late 1940’s. According to past studies, National Engineering Research and Development (NERD) Centre precast items are used in housing and other constructions and those details were collected by literature review. Objective of this study is updating the cost effectiveness of pre-cast concrete construction of a small-scale building. Data were collected from the NERD Centre and few pre-stressed concrete yards during the months of June to July 2019 by using face-to-face interviews. After the analysis of obtained data, it was found that there were differences between the costs of items among pre-cast concrete technology and conventional concrete construction technology. Overall cost comparison was done to single story building and two story building separately using cost rates of conventional method and pre-cast method. The outcome of the analysis revealed that, 30% - 38% of cost effectiveness can be obtained using pre-cast methods for single story building and using those methods for two story building, 29% - 32% of cost effectiveness can be gained.