The main goal of this attempt is to assess the quality of wine and determine if it is excellent or terrible. Humans have been engaging in the act of tasting for generations and have consistently made predictions using their sensory organs. A number of industries have recently embraced new technology and used it to a variety of uses. However, human knowledge is required in a number of businesses, such as product quality assurance. With the increasing popularity of the product, modern innovations have increased the cost of the treatment. To ensure the quality of the output, this study looks at a number of machines learning algorithms, including Random Forest Classification, Decision Tree classifiers, and Support Vector Machines (SVM). These methods make use of the product's current attributes to simplify the quality control procedure.

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying one.The overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or expensive.This work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs.

We suggested a face recognition technique based on Decomposed Meta Batch Normalization (DMBN) for this procedure.The strategy consists of two steps: batch normalization and deep facial recognition.Face recognition therefore involves feature matching and feature extraction.The first step is to collect the face photos from the dataset.The representation is an image with a large number of channels for the Gaussian receptive map.By using supervised learning, we turn on a handful of the most distinct channels.The characteristics of the facial picture are taken second.The person's face and emotion are then recognised using feature classification.The look on the face is recognisable.Recognizing a person's face characteristics and expression is the main goal, along with minimising feature mismatching to increase process performance.

The present research paper posits a health record management system that is secure, and utilises machine learning and blockchain technologies to achieve optimal efficacy.The objective of the proposed system is to augment the security and confidentiality of medical records, while facilitating effective and precise analysis of health-related data.The present study presents a comprehensive methodology and implementation plan for the proposed system, which entails the amalgamation of blockchain and machine learning technologies.The assessment outcomes indicated that the suggested system offered a secure and efficient resolution for managing health records.The system under consideration offers a potentially viable resolution for the management of health records with enhanced security measures, thereby facilitating the secure and prompt access and analysis of patient information by healthcare practitioners.Additional investigation and advancement in this domain may result in enhanced and optimised resolutions for the management of healthcare data, tackling the obstacles associated with the management and safeguarding of health-related information.The present study makes a valuable contribution to the domain of healthcare data management by offering a thorough and reliable solution that can assist healthcare establishments in the management and safeguarding of health-related data.

Recently, numerous disaster-related studies have integrated Remote-Sensing (RS) technology alongside picture processing and conventional methods. One example is identifying earthquake damage to buildings in orthophotos. Common approaches to creating damage maps from RS pictures include automatic and visual methods. However, because of the need for manual sampling, the visual method is laborious. By extracting the defect features, the automatic technique can identify the damaged structure. Challenges to the widespread use of automatic methods are presented by the great variety of design approaches and the dynamic nature of real-world situations, such as variations in shadow and light. To better train neural networks with less interference from measurement noise, principal component analysis (PCA) is used. After PCA is applied to the DI values, an ensemble of neural networks is utilized to provide predictions about the location and severity of damage. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two widely used methods in machine learning, and the CRNN approach combines the two (RNNs). As compared to the CNN and RNN models, the proposed technique performs admirably.

The application of “Internet of Things” is a new phenomenon which has supported organisation, individuals and others to transform the manner in which the business operations and other aspects are being connected. The IoT based future technology in an effective manner. The implementation of Blockchain tend to contain more strong protections against any form of data leakage, tampering of the information Hence, it has been stated by many researchers that the application of Blockchain based security support in enhancing the future technology through IoT. This research focuses in understanding the opportunities and address key aspects related to security aspects through IoT in creating better future technology. The researchers tend to use both primary data and secondary data for performing the study, the researchers collate the data from the respondents through survey method, the analysis are then made using AMOS statistical software and meaningful interpretation were presented based on the analysis.

An upsurge in suicide instances throughout the world is frequently caused by depression. Therefore, a precise evaluation and counselling are essential to alleviate the consequences of depression. The electrical activity of the brain is monitored and documented using an electroencephalogram (EEG). It is capable of producing a reliable evaluation of the degree of depression. Prior investigation established the viability of using deep learning (DL) models with EEG data to diagnose mental disorder. The patient's behaviour exhibits the signs of depression. As a result, doctors employ questionnaires and talking sessions as screening methods to determine the severity of depression. DeprNet is a DL-based Convolutional Neural Network that this research suggests be used to categorise the EEG data of healthy and depressed patients. The suggested system uses a convolutional neural network as a deep learning technique. The system was created using an EEG dataset for depression, and it analyses if a subject is positive, negative, or neutral. The confusion matrix, accuracy, precision, recall, and f1-score are among the experimental findings.

Voice recognition and classification involves the analysis of various speech signals with the goal of enhancing the accuracy of either human recognition or machine decoding.Utilizing characteristics and feature matching, speech recognition algorithms aim to enhance the performance of communication systems.The matching name of the item will thus be detected in this procedure based on the speech characteristics added to the voice signal used as the input question.Also, a number of web-based picture annotation tools have been developed to enhance the quality of image retrieval in response to the rise of social web apps and the semantic web.The primary goals of this procedure are to categorize the voice signal, get the picture from the dataset, and enhance feature classification.

Technology has become an important facet in every field. There is risk and challenge evolved through technology. Cyberbullying is an important concern in technological development. In the past ten years, cyberbullying has been recognized as a significant issue affecting young people. This paper summarizes recent research and addresses broader ideas. It addresses definitional issues like repetition and power imbalance, as well as different types of cyberbullying, age, and gender differences, overlap with traditional bullying, distinctions between cyberbullying and traditional bullying, the causes and effects of cyber victimization, coping mechanisms, and prevention and intervention options. This study emphasizes how Artificial intelligence can address cyberbullying issues in society. The study finds out problems regarding cyberbullying, role of AI in bullying and future recommendation. Society is victimized by these issues which are increasing day by day. The study emphasized a proposed model for addressing issues with the help of AI. The study emphasized futuristic challenges about cyberbullying.

The given study focuses on the influence of artificial intelligence (AI) on adjudicatory procedures mostly on observance of such right to an impartial trial in several law-related fields with respect to existing decisions. The consideration of such substance of the right to an impartial trial serves as the beginning stage for the debate, with a special focus on the autonomy and impartiality of the judiciary. Following that, the article analyses the prospect of minimizing the individual role in court processes in charge of making decisions using information technologies. This paper analyses the admissibility of using AI tools in the judiciary and contains considerations on ethical aspects of AI application in judicial proceedings and whether an AI system can take over the role of a decision-maker in judicial proceedings, thereby replacing or supporting the judge. The paper with a view of its legal compliance, non-discrimination, transparency, and efficiency of legal proceedings. The study employs the method of analyzing the law in force and investigating theories of law, taking into account the critical analysis of the literature at the decision level.

Osteoporosis is a disease that affects the bones, which are a very important part of the human body. It has a tendency to lessen the volume of bones and, as a result, affect the micro architecture of bone tissues. For decades, a variety of imaging techniques have been used to evaluate and investigate the micro architecture of disturbed and damaged bones to be able to discover bone density inadequacies. Image enhancement ,filtering, c lassification, segmentation, and other preprocessing techniques are used in image processing to diagnose the afflicted bone structure and obtain crucial information about the distorted micro architecture pattern. In this research, we have gathered rudimentary knowledge of tissues like osteoblast and osteoclast as well as a comparison evaluation of few osteoporosis detection approaches based on image processing. The goal of this study is to determine the prevalence of osteoporosis and changes in bone mass as people become older, as well as to compare the bone health of seemingly healthy males, puberty of women, and menopause women. We used a 260-person ethical database, with 130 men, 80 women (before menopausal), and 50 women (after menopausal). Bone mineral density (BMD) was measured at the femoral neck using dual energy X-ray absorptiometry.

The banana peel contains Phenolic compounds, which has higher concentrations as compared to other fruits. These Phenolic compounds contain very rich concentration of antioxidant, antimicrobial and antibiotic properties. The amendment of banana is good in India as well as world. The banana has second main fruit crop in India. The Banana has belong to Musa family. It is grown in warmer areas. Banana contains potassium, vitamin B6, vitamin C and various antioxidants and phytonutrients. Alike the banana peel (BP) contains Iron, cadmium, chromium, Nickel, Copper, lead and Zinc conducting materials. By using conducting material a flexible conducting ionic polymer membrane developed. The purpose of this paper is optimum use of the nutrients which is present in banana peel (Bp). Banana peel is biodegradable material, when they get some moisture its get starts rotten which increased the production of the microorganism, causes disease. Through banana peel an ionic polymer membrane is developed which helps overcome the banana peel waste problem. Through impedance analyzer we measured the electrical property of the banana peel ionic polymer membrane. Electric properties like conductance, capacitance, real and imaginary impedances are measured. From 20 Hz to 10 mHz range frequency is used to measure all electric properties. By using this range different- different 1600 / electric values are calculate. All these values are plotted with the help of polynomials regression. The conductivity PVA/BP is enhanced from 3 X 10-8 S to 8 X 10-7S by increasing the concentration of banana peel from 1 ml to 4 ml. The composition BP/ PAV shows good conduction or low dissipation factor and low dielectric constant. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Technological Advancements in Materials Science and Manufacturing.

Wireless sensor network is still one of the widest research areas for the researchers, which is made of self-sufficient sensor nodes in a group called cluster to screen physical or ecological conditions of the environment. Energy utilization and routing are the central issues in remote sensor systems. Therefore, numerous conventions have been proposed to minimize the energy utilization of these sensor nodes. The fundamental objective of this exploration is to enhance the directing procedure. This paper presents evolutionary bacteria foraging optimization method, named as EBFO, which uses a fitness function to measure the combined energy of the all sensor nodes in the cluster to make sure overall energy of cluster lesser energy than combined energy. It provides better field placement and cluster head selection. The results show the improvement in the energy efficiency and routing process in WSN as compared to standard BFO.

Since December 2019, Covid-19 has impacted the daily life of people across the World. There are presently 24,495,193 active cases and they are still increasing. The only available solution to cope up with the pandemic is efficient monitoring of the infected people. Techniques like RT-PCR that rely on checking the genetic expression of coronavirus are time-consuming. An automated early diagnosis method for a Covid-19 patient is of utmost requirement. In this work, the radiographic images along with prevalent Artificial Intelligence (AI) methods especially Deep Learning (DL) methods have been used in priority, to detect Covid-19. In this work, DL-based automated techniques are employed to process the chest X-ray images for the detection of Covid-19. The authors investigated the capabilities of the three pre-trained CNN models, that is, VGG16, VGG19, Inception V3 for extracting the features from Covid-19 positive, pneumonia, and normal chest X-rays. Lastly, various ML classifiers are used to classify the Covid-19 positive images. The results obtained important biomarkers related to coronavirus disease. The accuracy of 97% is obtained using VGG16 and Inception V3.

Image compression is a kind of compression of data, which is used to images for minimizing its cost in terms of storage and transmission. Neural networks are supposed to be good at this task. One of the major problem in image compression is long-range dependencies between image patches. There are mainly two approaches to solve this problem; one is to develop a better residual patch-based encoder, and the second one is to create an entropy coder capable of collecting long-term dependencies inside the picture between patches. We address both the problems in this paper and fuse the two possible solutions to improve compression levels for a given material. Results of the simulation reveal that the new algorithm works much better in all parameters than in the current model.

This comprehensive review provides an extensive overview of the existing Time Series Forecasting technique. This survey is not restricted to any single time series analysis; it provides forecasting of time series in different areas like marketing prediction, weather forecasting, technology prediction, financial forecasting etc. In this paper, we have analyzed forecasting in some areas namely, load forecasting, wind speed forecasting, prediction of energy consumption and short-term traffic flow prediction. Various models are available for prediction among them Autoregressive Integrated Moving Average model (ARIMA) is seen as a universal mechanism, these discussed forecasting areas utilizes different models that are combined with ARIMA. Hybrid models are the combination of classical models and modern methods, like ARIMA (classical method) combines with Artificial Neural Network (ANN) as well as with Support Vector Machine (SVM) (modern models). Hybrid model’s performance is depending on the variety of data that are taken for forecasting.

In a wireless sensor network, concealing the location of the sink is critical. Location of the sink can be revealed (or at least guessed with a high probability of success) through traffic analysis. In this paper, we proposed an energy efficient technique for concealing sink location named EESLP (Energy Efficient Sink Location Privacy) scheme. Here we proposed an approach, in which we are concealing the sink location in such a way so that node energy utilization while securing sink in network can be minimum, to defending sink’s location privacy and identity when the network is subjected to multiple traffic analysis attack. EESLP designs the network area of coverage with multiple spots generating fake message traffic for fake sink location creation that resembles the traffic behavior that is expected to be observed in the area where the sink is located. To achieve this we select some sensors away from the actual sink location which act as fake sinks by generating dummy or fake packet. The simulation results prove that EESLP can improve network life time and QOS (congestion, throughput, packet delivery rate) of sensor network while protecting sinks Location privacy.