Reviewerly/exHarmony huggingface dataset:
An Enhanced and Effective Approach for Object Detection using Deep Learning Techniques

Nowadays the technology is enhancing day by day and moving towards the era of Artificial Intelligence. We are training and making them self-efficient to rely on themself and produce efficient output. Object detection and trailing is one of the most important steps in computer vision algorithm. The vigorous object detection is one of the biggest challenge due to variations in the scenes. Object Detection is important for positioning objects, analyzing behavior and finding the values of accuracy, precision and recall. Now in following approach we are detecting object using TensorFlow object detection API and models like CNN, KNN and logistic. The proposed approach is capable of detecting the object with proper accuracy.
Conversational Interfaces for Health Consultation

What we do when we get ill or sick, standard practice is we go to a doctor or sometimes we search on the internet and take medicines without consulting any doctor. What if you want to consult a doctor but there is no clinic nearby you? So considering all the real-time problems, Here is the solution –Healthcare chat-bot services using flask. The adduce idea is to create a model that can understand your problem or diagnose the disease and recommend the medication for free or minimal charges and not only recommend but also keep a record/track of your health on daily basis and if needed book your appointments with the doctors immediately. You can relate it with a medication consultant /book in your hand. Not only in general situations but also in pandemic situations, such technology will reach its peak usage, where it will be not possible to visit a doctor frequently.
Analysis on Digital Forensics Challenges and Anti-forensics Techniques in Cloud Computing

In the modern world of techniques, the computerized cloud-based digital data storing techniques will grows and they will be shifted to the online level of cloud-based environments. After getting the data online there are most of the issues are produced like hacking, data thefts, data breaches, data virtualization, data validations, user authentication, and more. Most of the cloud storage is closed due to non-payment on time and the data has remained. The data access of this cloud will not be able to handle by the admin or developer, after sometimes data will be leaked on the internet because not any user or admin will be able to handle this cloud. This research paper has a conclusion, solutions, and challenges that will affect the forensic investigation of cloud-based data. The anti-forensic techniques are very dangerous with all which is cause zero traces pieces of evidence or less trace evidence which is the effect on forensic investigations. Anti-forensics tactics are intended to thwart digital forensics investigators. They include deception and methods to deceive digital forensics investigators. Furthermore, fraudsters utilize anti-forensics technologies to conceal their tracks from computer forensics professionals following a data breach or malware campaign. This article defines anti-forensics and the main strategies used by attackers to conceal or delay forensics investigations.
Performance Analysis of Cloud Hypervisor using Different Workloads in Virtualization

In virtualized cloud computing systems, energy conservation is crucial since it may positively impact various factors, such as decreased operating costs, increased system effectiveness, and environmental preservation. Simultaneously, an energy-efficient task scheduling technique is a potential option for achieving these objectives. Unfortunately, mapping cloud resources to user requests to achieve high performance while reducing cloud resource energy consumption within a user-defined timeframe is a significant difficulty. This paper finds cloud hypervisor power and energy charters with power measurements, namely Amazon AWS, Microsoft Azure, and Google Clouds. We will employ computation- and network-intensive workloads to identify several cloud hypervisor power and energy features and the optimal multi-tenant cloud architecture. Finally, the result depends on the hypervisor's current operation state due to the workloads assigned. (1) Each hypervisor exhibits a different energy and power conception rate according to the workload. (2) On all systems, no single primary hypervisor outperforms the others in terms of current energy consumption, despite their energy efficiency being further matched with various workloads and workload levels. The result obtained from this experiment will be helpful for designers and data centre operators to find out which cloud hypervisor is best for their virtual machine scheduling.
A Structure for Taking Measurements Health Research by Tracking Dynamic Attempting to Control Information through IoT Devices (IoT)

The IoT is a network of physical, or “stuff,” that are linked to each other exchange data online. IoT-based technological advancement has been the subject of many study and investigations. This study demonstrates how IoT devices may be used for healthcare applications. The Network of Things-based personal wearable gadget developed for this project has now been utilised to collect data on antihypertensive, respiration, pulse rate, pulse, and other crucial indicators. To increase the use of the Internet of and something public health monitoring technology in the near future, components connected to a possible risk assessment should be examined after substantial data gathering.
Ethereum and Intelligent Systems Technologies for COVID-19

Beginning in 2020, Covid has increased as a result of a burst put on by a respiratory infection with a substantial peaking fatality rate. The unforeseen occurrence and unchecked global spread of the COVID-19 illness highlight the limitations of current healthcare systems in responding to emergencies affecting public wellness. In these conditions, innovative developments like public blockchain and intelligent systems (AI) have emerged as possible treatments for the covid epidemic. In particular, block chain may help with early identification to combat pandemics. With the measures put in place to prevent infection by wearing masks, social seclusion with a 6m radius, routine testing, and two vaccine doses. This system includes mask measurement, people identification, temp sensors, information tracking, in-person interaction locating, and the current state of a user's medical chart. With the development of technology and increased smartphone usage, illnesses may be tracked and their spread controlled. Considering that the expansion of the business sector's rehabilitation and its continued broad distribution of Covid, it is more crucial to adhere to the instructions to avoid contamination.
Performance Analysis of Median Filter in Comparison with Gabor Filter for Skin Cancer MRI Images Based on PSNR and MSE

Themain aim of this research is to filter Skin Cancer MRI images based on the image processing technologies using novel median filter algorithm and is compared with Gabor filter algorithm. This research contains two groups, each with a sample size of 20 with Gpower of 80 percent. The performance of the novel median filter is evaluated and the performance measurements such as PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error) are compared with the Gabor filter. According to the data obtained by simulating with matlab, the novel median filter's PSNR is 31.64, and its MSE is 9.094, whereas the Gabor filter's PSNR (Peak Signal to Noise Ratio) is 27.02, and its MSE (Mean Square Error) is 11.52. From the statistical analysis, it is observed that the significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{p} &gt; 0.05$</tex> and value of MSE (0.010) of the algorithm is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{p} &lt; 0.05$</tex> . In this study, it is found that the novel Median filter performs better than the Gabor filter in terms of PSNR and MSE.
Challenges of E-Learning and Implementing a Responsive E-Learning in Higher Education

The key to creating a single course that looks gorgeous and performs wonderfully across a range of stages of sizes is reactive training. Today's students utilize a variety of devices of different sizes and kinds, including desktop computers, smart phones, and tablets. The learners often switch between using one gadget and another during the day depending on comfort, simplicity of use, and places. Both groups of workers utilise their smartphones to gain new skills via training and education, with 87 percent of young people using up to three devices every day. Responsive education is the art of creating a single course that will function and look great across a variety of devices and screen sizes.
Finding Picking Point Using Straight Line Detection of Label Attachment Machine

We developed a new technique for detecting the position of labels using a SCARA Robot with the end-effector vacuum pad. The technique first found the image edge with a Canny detector, then selected straight lines and their intersections. It assumed that the label was basically rectangular in shape and roughly aligned so that the key edges were close to horizontal and vertical directions. The slope of the horizontal edge defined the rotational of the label. Otherwise, reducing the image tilting and the distortion by using a perspective transform matrix and thereafter converting the image coordinate into the SCARA robot coordinate. Through the factory environment, the experiment is conducted using various captured image conditions under a lighting source. The performance of the system provides the acceptable accuracy all of the picking coordinates with positioning errors, which x-coordinate are 0.01 – 0.25 mm, y-coordinate are 0.01 – 0.32 mm and rotation angle are 0.05° – 3.94°. In trials, the SCARA robot was able to grip the label precisely in each test.
Network Scientific Technique used in the Detection of Attack

It's miles vital to attend to the safety of net-based total facts systems. due to the fact, a community is public and global is largely no longer comfy. while data is dispatched from one non-public computer to another, the data could be in multiple private computer systems, giving the other consumer a possibility to thieve the records. It came about nearly every day all around the globe. One approach to stealing statistics is the man inside the center assault which attacks the server. The intrusion detection system is carried out with process sniffing, visitor records viewing, and open supply logs traffic snicker evaluation. The Intrusion Detection gadget analyzes all site visitors' systems inside the snicker community to sniff and stumble on many styles of cybercrime. research is carried out with a stay forensic method that has essentially the equal traditional forensic techniques as the identity of financial savings, evaluation, and presentation. This research hopes to attain facts together by placing a log with set chortle to personal pc to hit upon net server attack and then studying the log record to hint the evidence from the log laugh report to forensic digital detection. For. This study generates records in the shape of indicators from assaults displayed with the aid of IDS chortle that is already set up on web servers. The log document has analyzed the use of cord shards to look for virtual forensic proof in the form of an IP address that brought about the attack, while the attack passed off, how the assault passed off and where the assault befell. based totally on the implementation of IDS snort to detect a guy within the center attack. The effects of the search for a digital forensic proof are obtained within the form of IP addresses and ports utilized by attackers to get entry to internet servers. assaults are mitigated by blocking off the IP address and port utilized by the attacker to get entry to the web server. This research has been done efficaciously.
A Survey on Cryptocurrencies Utilizing the Blockchain

This study examines public perception of blockchains. The data is stored in block on the ledger, and each piece includes the checksum associated with the coin. After completing this scientific study, you will have a better understanding of the history of cash, an overview to the electronic cash system, how data is stored in blocks as cryptographic hashes, how various crypto currencies were created, and exactly how to utilise them. Additionally, bitcoin may be employed as a tradeable asset class. Numerous crypto trading platforms that provided crypto trading have existed since 2011. The general state of cryptocurrency is increasing every day.
Blockchain: Remaking the Healthcare Sector

Blockchain is used by the HRE (Healthcare Records Exchange) technology to satisfy the necessary requirements in line with the requirements of the public health system. Bitcoin, often known as a ledger occasionally, is a distributed system. a continually growing, linked to other bricks, protected store with lists of entries. Due to the decentralized characteristics of the system and the use of peer-to-peer (P2P) processor or network, it is ideal for the handling of critical material, such as patient information at a facility. The job is divided up across peers in a distributed application design for improved flow. Here, every peer is an equal participant in the programmed with the same privileges. the peer-to-peer or node-based network form A programmable contract containing the terms between the parties, incorporated inside the decentralized Blockchain network, is known as a smart contract. In the near future, agreements and Blockchain - based will be used to compare medical research data with other hospital databases where there is a larger chance of a data breach. Blockchains provides one of the safest ways to safeguard data with little restrictions, therefore this risk may be reduced.
Super Six - Axis Cord Influenced Linear Laser for Personal Flower Management in Cultivation with Live Control Systems is Connected to The Robot Manipulator

In today's agribusiness, using robotics, machine learning, and also the web of things has become standard practise. The agricultural business is being forced to embrace such new methods in order to address issues like massive unemployment, rising food consumption with declining quality farmland, efficiency per square land, and deteriorating food quality due to global warming. As a result of the aforementioned difficulties and the world's increasing population, research and innovation in agricultural technology have become more crucial nowadays. In this document, we suggest an AI-powered wire automaton with six levels of autonomy and an omega automaton with degrees of opportunity that can help with agricultural tasks like sorting, pest management, picking strawberries and flowers, fertiliser application, and tracking of plant development, NPK tiers, and soil moisture. The robotic is called Farmbot. The Based criteria may communicate crop information to distant servers where it can be processed and retrieved to get further insight into soil quality. It can also be managed and watched remotely by a home computer or via a mobile internet software. The Farmbot may be programmed to do routine tasks including continuous monitoring, collecting fruit, cleaning, applying fertiliser, and gathering information about each plant. The Farmbot has direct exposure to just about any area of the field thanks to its placement over a moveable four wheels bot. This Farmbot's goal is mainly on providing individualised plant maintenance and aiding producers in raising output. As a design illustration of a functioning version for the imagined Farmbot, a 3D depiction is shown. The simulation's outcomes are shown.
A Multiple Regression Approach was used to Conduct A Critical Evaluation of a Home Automation that used the Natural Language Processing and Effective Tool for Improving

The Web of Things is primarily regarded as one of the primary technological instruments that permits interconnecting various devices over the network, enabling comprehensive data and data interchange, supporting the receipt of instructions, and enabling efficient response to those instructions. With the development of IoT, a large number of devices are now linked to the internet, allowing users to exchange data, programme desired actions, and control the gadget remotely. The goal of this research is to better comprehend the fundamental factors that influence the development of smart homes via the Internet of Things (IoT). Integration of speech recognition into gadgets, remote device control, and assistance with energy bill reduction are the main factors taken into account. The research aims to gather evidence of both secondary source in order to conduct the study. The information is numerically applied using a five - point scale after being gathered through a quiz. The experts use big data analytical methods known as AMOS to understand the route network using the concept of covariance - based structural equation.
A Novel Approach for Multi-Document Summarization using Jaccard and Cosine Similarity

Text summarization is process of getting summaries of multiple documents. Text summarization is very much essential for saving time and resources. Through text summarization knowledge can be grasped quickly. Getting summery is challenging process because relevant information needs to be retrieved. Text summaries essential to resolve the issues of information overload which demands access to reliable and properly crafted summaries. Users can quickly find the information they need using data minimization. Saving the time and effort from having to browse through the entire collection of documents is main advantage of text summarization. In this paper it is focussed on an extractive technique of text summarization using a sentence clustering approach. The hybrid approach deals with the novel method for comprising of document and sentence clustering using Jaccard and Cosine similarity method.
Emotion Detection of Thai Elderly Facial Expressions using Hybrid Object Detection

An elderly population is a special group that needs to be taken care of closely. A key area of concern for the elderly is that of mental health and many technologies can be applied in this area. One possible tool is facial expression recognition (FER) that can be used to detect emotions of the elderly for the purpose of mental health care. In this research, we propose a hybrid of Faster R-CNN, SSD, and YOLOv5 object detection models for elderly facial expression detection. In our experiments, the proposed hybrid model was trained on a Thai elderly facial emotion dataset, and its performance was compared to a single-model of Faster R-CNN, SSD, and YOLOv5. The experimental results indicated that the proposed hybrid object detection model had achieved the best performance with an accuracy of 94.07%. This was comparatively better than YOLOv5, which gave the accuracy of 93.33%.
A Survey of Machine Learning Based Approaches for Neurological Disorder Predictions

Novel computational tools based on ML schemes are useful in knowing the complex brain functions and its diseases. It was found during the study that differentiation among various neurological disorders is not easy task due to similarities in symptoms. This paper significantly examines and compares performances of many ML based methods to diagnose neurological illness—emphasized on Alzheimer's disease, Parkinson's disease and schizophrenia. The article provides the overview of computational intelligence methods evaluates and diverse performance metrics used to predict neurological disorders from different type of data.
Tag Recommendation for Movie and Music Contents

Tag recommendations are popular for labelling online web content. It usually involves the process of content classification, organization, and information retrieval. With the increase in the consumption of online content, recommender systems have been developed to study the behavioural patterns of users and use these results to recommend content. The cold start problem is an issue being faced in developing recommender systems due to insufficient data regarding recommended items. In this paper, we propose a hybrid approach of a Media Convolutional Neural Network with a Latent Dirichlet Allocation topic modelling (MCNN-LDA) algorithm for recommending tags for movie and music contents in trying to address the multi-label classification issue of the cold start problem. The final model is evaluated on the Internet Movie Database (IMDB) movie dataset and GTZAN audio dataset. The high accuracy results of the developed model achieved at a threshold of 0.90 indicate a high performance on the quality of tags recommended which is confirmed by the evaluation metrics performed.
The Application of Grow Model in Vocal Music Teaching

With the overall progress and development of our society, school music education has become an important part of school art education, and music education in Colleges and universities has also moved towards a new stage of development. This paper mainly discusses the practical significance of vocal music teaching in Colleges and universities, briefly summarizes the development history and current situation of vocal music teaching in Colleges and universities in China, defines the nature and tasks of vocal music teaching in Colleges and universities, further discusses the value of vocal music teaching in Colleges and universities, and explores in detail the vocal music teaching curriculum in Colleges and universities from four aspects: teaching purpose, teaching method, teaching principle and teaching evaluation, This paper investigates and analyzes the current situation and existing problems in the implementation of teaching contents in Colleges and universities, and makes an in-depth study on the implementation of comprehensive teaching contents in Colleges and universities mainly from the aspects of teaching contents such as the training of skills and skills, the cultivation of vocal music emotional ability, the cultivation of vocal music aesthetic ability and the expansion of vocal music culture.
Using compression tables to improve HiveQL Performance with Spark A Case study on NVMe Storage Devices

Query language execution is widely used in big data. The SQL standard is the major query language. Big data has a lot of SQL-like tools, for example: Spark-SQL, Hive, Drill, and Presto. This paper focused on Hive with the Spark engine. To increase Hive’s query performance in a case study, NVMe Solid State Devices, we proposed the compressed Parquet file including SNAPPY, gzip, and Zstandard (zstd). Query workloads use TPC-H benchmark. Thus, this compression codec can reduce the main transaction table of TPC-H benchmark by 56%, and some queries have lower CPU usage than Text file. However, the Hive on Spark engine with our proposed compression codecs for Parquet files has lower CPU usage than Text file in some TPC-H queries. Thus, NVMe storage with the Parquet file compression codec is more efficient than text files for improving query performance on the Spark engine.