European Journal of Molecular & Clinical Medicine

Cancer has manifested itself as one of the most serious health problems facing humanity today. Cervical cancer is one of the most common types of cancer, and the Papanicolaou (Pap) test, often known as a Pap smear, is the most basic test for cervical screening. It involves the microscopic inspection of cervical cells obtained from the cervix, and it is performed by a physician. In order to achieve this, automated detection and classification of cervical cancer from pap-smear images has become a must just because it allows for accurate, reliable, and rapid investigation of the condition's progression. With an emphasis on the history of pap screening, liquid based cytology, and machine learning for cervical cancer detection in recent research publications, this paper provides a summary of the state of the art as stated in several significant recent information sources. For the first time, an evaluation of image analysis and machine learning applications in the growing trends of cervical cancer diagnosis from pap-smear images over the course of a decade has been published. The survey examines 26 journal papers that were obtained electronically through major scientific databases such as PubMed, Google Scholar, Scopus, IEEE, and Science Direct, which were searched using sets of keywords. The papers were obtained from major scientific databases such as Pubmed, Google Scholar, Scopus, IEEE, and Science Direct. Whenever the Pap test is improved through the use of artificial intelligence, the sensitivity for the detection of cervical pathology is improved as well. The general public should be taught about the Pap smear test with AI, including its purpose and the frequency with which it must be performed, through comprehensive programmes aimed at improving disease management in general.

Depression strikes different people regardless of their social status, education level, or
gender. For this reason, it is important to detect this disease as soon as possible to
avoid negative consequences in people who suffer from it . This study is a review of
scientific literature, where 200 articles have been collected from the following
databases: Ebsco Host, IEEE Xplore, Science Direct and Scopus.Based on our
inclusion and exclusion criteria, 40 articles were systematized. Having good results
on the topic of the most common intelligent systems and the approach that is
recommended when developing an intelligent system.

This review discusses a role for psychology in the development in the sector of artificial intelligence. Cognitive science bestows the scientific basis for advancement in the realm of artificial intelligence. Gleaning a high tech machine that can think ,learn, reason, experience and can function autarchic bereft of supervision is one of the pivotal grails in all of computer science. Studies posit that higher education in psychology can smooth the path for reasoning about general issues. With a prodigiously autarchic, learning ,reasoning,artificially  intelligent system comes with a need to possess hardware and software that transcribes processes and subsystems  that subsist within a human brain including intuitive and emotional concepts. The idea of interweaving these two immense realms: the complexities of psychology and vitality of artificial intelligence has gained escalation in recent times. This review focuses on how computer implementation and psychological tools bring enhancement in the field of artificial intelligence.

A text classification is a well formed process using various measurable properties and computerized logical procedure to fetch a pattern from different classes.Since classification is important for the pattern recognition process, there are some issues with well-formed classification in this process, which is one of the important issues for proper development and improvement of productive data examinations. On behalf of the versatility of learning and the ability to deal with complex calculations, classifiers are consistently best suited for design patter recognition issues. The aim of this paper is to present a result based comparative study of different classifiers and the optimal recognition of results computation through the Devanagari Handwritten characters and numerical values. Different classifiers were used and evaluated in this investigation including k-Nearest Neighbor (k-NN), Support-Vector machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Convolution Neural Network (CNN). To accomplish the experiment purpose, this paper used an unbiased dataset with including 123 samples that consists of 123 characters and 123 numerical values. Python 3.0 with sciket learn machine learning open-source environment library have been used to evaluate the performance of the classifiers. The performances of the classifiers accessed by considering the different matrices including dataset volume with best split ratio among training, validation, and testing process, accuracy rate, Ture/False acceptance rate, True/False rejection rate and the area covered under the receiver operating characteristic curve. Similarly the paper shows the correlation of the accuracy of the experiments obtained by applying to chosen the classifier. On behalf of the exploratory results, the
infallible classifiers considered in this test have free rewards and must be executed in a hybrid manner to meet the thigh precision rates.In the views of test work, their result compressions and the examination to be performed, it is argued that the Random Forest classifier is performing in a way that the current use of the classifier to recognize the Devanagari Handwritten character and the numerical values with the accuracy rate 87.9% for the considered 123 samples.

This review article covers the current research areas in oral pathology and reflects the broad range that encompasses the development and application of software in digital histopathology, implementation of biomarkers in pathology, genetics and epigenetics. Molecular pathology, regenerative medicine and immuno therapy, holds the promising and optimistic future of pathology. Oral and maxillofacial pathology is standing at the forefront of the revolution and new diagnostic tools and knowledge are taking pathologists into broader roles of research and correlating diagnoses for clinicians. While we are still using the same method and material that has been using for the past 100years, it’s time to change. Digital technologies could push the field into becoming more efficient and more scalable. Utilizing  high-throughput, automated digital pathology scanners, it is possible to capture an entire glass slide, under bright field or fluorescent conditions, at a magnification comparable to a microscope. Digital slides can be shared over networks using specialized digital pathology software applications. The future of digital pathology could eventually encompass enhancedtranslational research, computer aided diagnosis (CAD) and personalized medicine.

Non-functional requirements play a critical role in choosing various alternative model and ultimate implementation criteria. It is extremely significant in the earlier stages of software development that requirement engineering produces successful technology and eliminates system failure. The recent work has shown that the automated extraction and classification of quality attributes from text files have been demonstrated by artificial intelligence approaches including machine learning and text mining. In the automated extraction and classification of nonfunctional specifications, we suggest a supervised categorization approach. To test our approach to obtain interesting outcomes, a very well-known dataset is used. In terms of security and performance, we obtained a specific range of 85% to 98% and obtained a best result together for security, performance and usability.

In today’s world, digitization plays an extremely prominent role in day-to-day applications.Its future deployment, needs an Internet of Things (IoT) to embrace automation, remote monitoring and predictive analysis. IoT is a device connected with an internet and it’s a combined embedded technology including actuator and sensor device. Also, it encompasses, wired and wireless communication devices, and real-world physical objects connected to the
internet. IoTis majorly used in diversified fields like smart classroom, smart banking, smart home, smart agriculture, smart healthcare application etc. Typically, IoT requires intelligence, to achieve theautomation process in an efficient way in many applications. Artificial Intelligence (AI) paves the way to makes the IoT smarter and efficient by its approaches. Due to enormous amount of data being generated in various applications, IoT combined with Machine Learning(ML) and Deep Learning(DL) models is used to enhance the functionality in complex applications. In this survey the applicationof AI, ML and DLmodels deployed in IoT are deeply explored.

A large number of researches have been conducted in the field of AI. This paper is all about the enhancements made in this popular field. Making a machine that is able to understand the background ideas of the words is very essential as it can increase the chances of better translation as well as can execute conversations as humans do. In particular, this paper states the difference between the AI and the Non-AI tasks. The work is generated for new candidates coming in the area of AI as well as some issues related to AI are also talked about.

Artificial Intelligence is a progressive, rapidly developing field. It has the potential to ease diagnosis, treatment and care of patients. It is vital that medical professionals are aware about Artificial Intelligence and its scope in health care. Artificial Intelligence has the capability to ease diagnosis and care. The future of medicine is Artificial Intelligence based. A survey was carried to assess the awareness levels about Artificial Intelligence and its scopes in Healthcare. A questionnaire was circulated among medical and healthcare professionals. The total participation for the study was 100. The results were collected and tabulated to be analysed using SPSS windows version 20. There was an overall positive response from the participants. 92% believed that Artificial Intelligence is the future of medicine. Recent advancements have made it imperative that the healthcare workers be aware of Artificial intelligence and its scopes. Artificial Intelligence is reaching the medical field. It is a reality that a certain level of hyperbole seems to have taken over the discussion of Artificial Intelligence in healthcare. On one hand, healthcare industrialists and researchers highlight the need for high quality health data, on the other hand, physicians are still waiting for evidence of the usefulness of these tools and wonder who will be held responsible in case of an injury due to the tool, and the other ethical factors associated with it. It can be concluded that the participants had a moderate level of knowledge about Artificial Intelligence and its scopes, which can be improved.