Automatic Color Extraction Algorithm of Graphic Design Image Based on Artificial Intelligence

The extraction of color features plays an important role in image recognition and image retrieval. In the past, feature extraction mainly depends on manual or supervised learning, which limits the automation of the whole recognition or retrieval process. In order to solve the above problems, an automatic color extraction algorithm based on artificial intelligence is proposed. According to the characteristics of BMP image, the paper makes use of the conversion between image color space and realizes it in the visual C++6.0 environment. The experimental results show that the algorithm realizes the basic operation of image preprocessing, and realizes the automatic extraction of image color features by proper data clustering algorithm.

CAPTCHA for crowdsourced image annotation: directions and efficiency analysis

Purpose Image annotation plays an important role in image retrieval process, especially when it comes to content-based image retrieval. In order to compensate the intrinsic weakness of machines in performing cognitive task of (human-like) image annotation, leveraging humans’ knowledge and abilities in the form of crowdsourcing-based annotation have gained momentum. Among various approaches for this purpose, an innovative one is integrating the annotation process into the CAPTCHA workflow. In this paper, the current state of the research works in the field and experimental efficiency analysis of this approach are investigated.Design/methodology/approach At first, and with the aim of presenting a current state report of research studies in the field, a comprehensive literature review is provided. Then, several experiments and statistical analyses are conducted to investigate how CAPTCHA-based image annotation is reliable, accurate and efficient.Findings In addition to study of current trends and best practices for CAPTCHA-based image annotation, the experimental results demonstrated that despite some intrinsic limitations on leveraging the CAPTCHA as a crowdsourcing platform, when the challenge, i.e. annotation task, is selected and designed appropriately, the efficiency of CAPTCHA-based image annotation can outperform traditional approaches. Nonetheless, there are several design considerations that should be taken into account when the CAPTCHA is used as an image annotation platform.Originality/value To the best of the authors’ knowledge, this is the first study to analyze different aspects of the titular topic through exploration of the literature and experimental investigation. Therefore, it is anticipated that the outcomes of this study can draw a roadmap for not only CAPTCHA-based image annotation but also CAPTCHA-mediated crowdsourcing and even image annotation.

Distinct neural mechanisms support different forms of inner speech

Humans have the ability to mentally examine speech. This covert form of speech production is often accompanied by sensory (e.g., auditory) percepts. However, the cognitive and neural mechanisms that generate these percepts are still debated. According to a prominent proposal, inner speech has at least two distinct phenomenological components: inner speaking and inner hearing. Here we use transcranial magnetic stimulation to test whether these two phenomenologically distinct processes are supported by distinct cerebral mechanisms. We hypothesise that inner speaking relies more strongly on an online motor-to-sensory simulation that constructs a multisensory experience, whereas inner hearing relies more strongly on a memory-retrieval process, where the multisensory experience is reconstructed from stored motor-to-sensory associations. We predict that the speech motor system will be involved more strongly during inner speaking than inner hearing. This will be revealed by modulations of TMS evoked responses at muscle level following cortical stimulation of the lip primary motor cortex.

Development of Textual Analysis using Machine Learning to Improve the Sentiment Classification

Rapid development in Internet and the increase in online information, the technology demanded for intelligently classifying the textual data has become significant role in Information Retrieval Process. Based on given query in the search box, the response from the internet has made open to the public. Thus, the scope of text mining is being explored by several researchers. Sentiment Analysis is one of the most popular process for analysing user opinions and feelings, and since, online communication has become the fast ever growing medium for expressing thoughts, therefore, there have been development in text classification to improve sentiment analysis. In this paper, some of the prior works on sentiment analysis and the advancements in text classification have been discussed.

Information Retrieval based on Cluster Analysis Approach

The huge volume of text documents available on the internet has made it difficult to find valuable information for specific users. In fact, the need for efficient applications to extract interested knowledge from textual documents is vitally important. This paper addresses the problem of responding to user queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a cluster-based information retrieval framework was proposed in this paper, in order to design and develop a system for analysing and extracting useful patterns from text documents. In this approach, a preprocessing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector Space Model (VSM) is performed to represent the dataset. The system was implemented through two main phases. In phase 1, the clustering analysis process is designed and implemented to group documents into several clusters, while in phase 2, an information retrieval process was implemented to rank clusters according to the user queries in order to retrieve the relevant documents from specific clusters deemed relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision (P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.

PENGAPLIKASIAN JOB SAFETY ANALYSIS (JSA) DAN METODE RAPID UPPER LIMB ASSESSMENT (RULA) PADA PEKERJAAN MANUAL HANDLING

Manual Handling is the process of lifting, moving, placing, pushing, pulling, sliding, and supporting loads with the hands and body. There are 5 work processes at PT. Aura Mandiri Sejahtera still manually which is identified as an ergonomic hazard due to a mismatch between the tool or machine and the posture of the workers. The 5 work processes are the Stone Retrieval Process, the Stone Collection Process, the Stone Lifting Process to the Car Body, the Sand Extraction Process, and the Sand Lifting Process to the Car Body. The purpose of this study was to identify the hazards of manual handling work using JSA and to assess the risks of manual handling work using the RULA method at PT. Aura Mandiri Sejahtera. This type of research is a qualitative descriptive research was conducted from February to August 2021 at PT. Aura Mandiri Sejahtera. The results showed that from 5 manual handling work processes at PT. Aura Mandiri Sejahtera there are 5 ergonomic hazards identified using JSA and when a work risk assessment is carried out using the RULA method, it is found 4 work process with a score of 7 that means efforts must be made to change the work process immediately. for change efforts are expected to the leadership of PT. Aura Mandiri Sejahtera carries out work risk control by modifying work tools in the form of providing or replacing work equipment in accordance with the worker's body posture

A multistage retrieval account of associative recognition ROC curves

Despite its name, associative recognition is a paradigm thought to rely on memory recall. However, it remains unclear how associative information may be represented and retrieved from memory and what its relationship to other information, such as item memory, is. Here, we propose a computational model of associative recognition, where relational information is accessed in a generic, multistage retrieval process. The model explains the relative difficulty of associative recognition compared with item recognition, the difference in experimental outcomes when different types of lures are used, as well as the conditions leading to the emergence of associative ROC curves with different shapes.

Contextual Image Illustrator: A Review

Any story or any other literary content is best understood and advertised with the help of pictures. Images are used to arouse reader’s interest and comprehension in the content. The contextual image illustrator will take any content description and will output the ranked images related to that content. The text can be any blog, newspaper article, any story or any other content. The image retrieval process that has been used for this purpose is Text based Image Retrieval, i.e., TBIR. Semantic keywords are extricated from the story; images are looked through an annotated database. Thereafter, an image ranking scheme will determine the relevance of each image. Then the user can choose among the images displayed. A score along with each image will also be displayed representing its relevance to the query. The keywords stemming and stop word removal has been explained in the document. Also, the algorithm that has been designed to determine the score and hence the image’s significance has been calculated. Testing consisting of both unit testing and module testing of the project are explained. Keywords: Keyword Extraction, Image Search, Stemming, Stop word Removal, URL Score, URL Ranking

UV-Vis Spectrophotometric Analysis of DNA Retrieval for DNA Storage Applications

Informational Deoxyribonucleic Acid (iDNA) has gained the attention of many researchers and pioneer companies for the development of novel storage systems for the long-term and high-density storing of information. This research focuses on the physical storage of iDNA strands to address some of the current challenges by evaluating the accuracy of the process of iDNA retrieval from the surface after the dehydration process. For this aim, a UV-Vis spectrophotometric technique was used to measure the concentration of the DNA samples. Although spectroscopy has been widely employed for the evaluation of DNA concentration and contamination in a solution, it has not been used to investigate dry-state DNA, which is one of the preferred storage formats for the long-term retention of information. These results demonstrate that the UV-Vis spectrophotometric technique can be used to accurately measure dry-state DNA before the retrieval and its residues after the DNA retrieval process. This paper further examines the storage/retrieval process by investigating the relationship between the storage time and the amount of retrieved DNA or the DNA residue left on various surfaces. Based on the experimental results demonstrated and discussed in this paper, UV-Vis spectrophotometry can be used for monitoring dry-state DNA with a high accuracy larger than 98%. Moreover, these results reveal that the hydrophilicity and hydrophobicity of the surface do not significantly affect DNA retrieval over a one-month time period.