Predicting disease at an early stage becomes critical, and the most difficult challenge is to predict it correctly along with the sickness. The prediction happens based on the symptoms of an individual. The model presented can work like a digital doctor for disease prediction, which helps to timely diagnose the disease and can be efficient for the person to take immediate measures. The model is much more accurate in the prediction of potential ailments. The work was tested with four machine learning algorithms and got the best accuracy with Random Forest.

Computing semantic similarity between two words comes with a variety of approaches. This is mainly essential for applications such as text analysis and text understanding. In traditional systems, search engines are used to compute the similarity between words. In that sense, search engines are keyword-based. There is one drawback that users should know what exactly they are looking for. There are mainly two main approaches for computation, namely knowledge-based and corpus-based approaches. However, there is one drawback that these two approaches are not suitable for computing similarity between multiword expressions. This system provides an efficient and effective approach for computing term similarity using a semantic network approach. A clustering approach is used in order to improve the accuracy of the semantic similarity. This approach is more efficient than other computing algorithms. This technique can also be applied to large-scale datasets to compute term similarity.

The Internet is the largest source of information created by humanity. It contains a variety of materials available in various formats, such as text, audio, video, and much more. In all, web scraping is one way. There is a set of strategies here in which we get information from the website instead of copying the data manually. Many webbased data extraction methods are designed to solve specific problems and work on ad hoc domains. Various tools and technologies have been developed to facilitate web scraping. Unfortunately, the appropriateness and ethics of using these web scraping tools are often overlooked. There are hundreds of web scraping software available today, most of them designed for Java, Python, and Ruby. There is also open-source software and commercial software. Web-based software such as Yahoo! Pipes, Google Web Scrapers, and Firefox extensions for Outwit are the best tools for beginners in web cutting. Web extraction is basically used to cut this manual extraction and editing process and provide an easy and better way to collect data from a web page and convert it into the desired format and save it to a local or archive directory. In this study, among other kinds of scrub, we focus on those techniques that extract the content of a web page. In particular, we use scrubbing techniques for a variety of diseases with their own symptoms and precautions

Virtual machines are popular because of their efficiency, ease of use, and flexibility. There has been an increasing demand for the deployment of a robust distributed network for maximizing the performance of such systems and minimizing the infrastructural cost. In this study, we have discussed various levels at which virtualization can be implemented for distributed computing, which can contribute to increased efficiency and performance of distributed computing. The study gives an overview of various types of virtualization techniques and their benefits. For example, server virtualization helps to create multiple server instances from one physical server. Such techniques will decrease the infrastructure costs, make the system more scalable, and help in the full utilization of available resources.

Attention-based transformer models have achieved state-of-the-art results in natural language processing (NLP). However, recent work shows that the underlying attention mechanism can be exploited by adversaries to craft malicious inputs designed to induce spurious outputs, thereby harming model performance and trustworthiness. Unlike in the vision domain, the literature examining neural networks under adversarial conditions in the NLP domain is limited and most of it focuses mainly on the English language. In this article, we first analyze the adversarial robustness of Bidirectional Encoder Representations from Transformers (BERT) models for German data sets. Second, we introduce two novel NLP attacks: a character-level and a word-level attacks, both of which utilize attention scores to calculate where to inject character-level and word-level noise, respectively. Finally, we present two defense strategies against the attacks above. The first implicit character-level defense is a variant of adversarial training, which trains a new classifier capable of abstaining/rejecting certain (ideally adversarial) inputs. The other explicit character-level defense learns a latent representation of the complete training data vocabulary and then maps all tokens of an input example to the same latent space, enabling the replacement of all out-of-vocabulary tokens with the most similar in-vocabulary tokens based on the cosine similarity metric.

Since automatic knowledge extraction must be performed in large databases, empirical studies are already showing an explosion in the search space for generalized patterns and even more so for frequent gradual patterns. In addition to this, we also observe a generation of a very large number of relevant extracted patterns. Being faced with this problem, many approaches have been developed, with the aim of reducing the size of the search space and the waiting time for detection, for end users, of relevant patterns. The objective is to make decisions or refine their analyses within a reasonable and realistic time frame. The gradual pattern mining algorithms common in large databases are CPU intensive. It is a question for us of proposing a new approach that allows an extraction of the maximum frequent gradual patterns based on a technique of partitioning datasets. The new technique leads to a new, more efficient hybrid algorithm called MSPGrite. The experiments carried out on several sets of known datasets justify the proposed approach.

Precision agriculture relies heavily on information technology, which also aids agronomists in their work. Weeds usually grow alongside crops, reducing the production of that crop. They are controlled by herbicides. The pesticide may harm the crop as well if the type of weed is not identified. To control weeds on farms, it is required to identify and classify them. A convolutional network or CNN, a deep learning-based computer vision technology, is used to evaluate images. A methodology is proposed to detect weeds using convolutional neural networks. There were two primary phases in this proposed methodology. The first phase is image collection and labeling, in which the features for images to be labeled for the base images are extracted. In the second phase, the convolutional neural network model is constructed by 20 layers to detect the weed. CNN architecture has three layers, namely, the convolutional layer, the pooling layer, and the dense layer. The input image is given to a convolutional layer to extract the features from the image. The features are given to the pooling layer to compress the image to reduce the computational complexity. The dense layer is used for final classification. The performance of the proposed methodology is assessed using agricultural dataset images taken from the Kaggle database.

Sentiment analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect and emotion-based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data. We start by training Word2Vec word embeddings for Hindi HASOC data set and Bengali hate speech (1) and then train long short-term memory and subsequently employ parameter sharing-based transfer learning to Bengali sentiment classifiers, by reusing and fine-tuning the trained weights of Hindi classifiers, with both classifiers being used as the baseline in our study. Finally, we use BiLSTM with self-attention in a joint dual-input learning setting where we train a single neural network on the Hindi and Bengali data sets simultaneously using their respective embeddings.

The main aim of this study is to reframe the possibilities in healthcare with the aid of blue brain technology. In general, blue brain is usually associated with the preservation of the intelligence of individuals for the future. This study has stepped ahead by describing the other possible solutions that can be provided by implementing the blue brain technology in the medical field. The possibilities for decreasing the demise rates that occur due to the complications in the brain have been discussed. The blue brain can be used for monitoring the conditions of the brain, based on which the brain diseases can be diagnosed and cured in advance. In this study, the details about the blue brain, its functions, simulations, and upgradations of the human brain are explored in depth. The future enhancements and predictions in the field of the blue brain that can benefit humanity are also being discussed in this study.

Text mining and nature language processing (NLP) have become important tools in many research areas. Text mining is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. This task conducted a series of text mining jobs mainly based on the New York Times news titles corpus from January 2020 to April 2021. This task also did some analyses based on the US congressional speeches during the same period. The result shows that compared with the focuses of US congressional speeches, the focuses of New York Times news titles better reflected the changing hotspot issues over time.

Online Teaching Learning (OTL) systems are the future of the education system due to the rapid development in the field of Information Technology. Many existing OTL systems provide distance education services in the present context as well. In this paper, several types of existing OTL systems are explored in order to identify their key features, needs, working, defects and sectors for future development. For this, different aspects, types, processes, impacts, and teaching-learning strategies of various OTL systems were studied. In addition, the paper concludes with some future insights and personal interest in the further development of OTLs on the basis of previous research performed

High-altitude ballooning, along with other aerospace endeavors, requires extensive preplanning and preparation for vehicle launching. In ballooning specifically, weather conditions are especially effective and driving whether or not a launch can occur. As most flights must be shaped around the flight path, both for safety and recovery reasons, it is imperative that any acceptable flight path and day may be considered. The goal of this project is to minimize, using machine learning, the complexity and manpower requirements for determining if a launch can occur.

The trend of data breaches globally is attracting concerns from business owners and the government of different countries. Notwithstanding the multiple pieces of legislation and strict requirements for reducing crime, there has been an exponential increase in data breach incidents. These crimes result in the loss of billions of dollars annually from small entities and large enterprises. Data breaches did not only start during the digital era with technological advancement; they started when individuals and organizations stored and maintained their data and records onpremises. However, as technology advances and computer systems become more accessible and affordable, coupled with poor management of sensitive documents, data breaches occur when individuals view other people’s files without authorization. The rate of data breaches rose from 1980 to the early 2000s, giving rise to awareness of the canker. Laws and regulatory agencies such as Health Insurance Portability and Accountability Act (HIPAA) and Payment Card Industry (PCI) Data Security Standards were then established to guide sensitive data and the custodians. Regulatory frameworks developed as best practices to secure sensitive information are, however, not being implemented exhaustively, thereby not able to gatekeep satisfactorily. Data breach frequency is high in the digital era. The digital era exposes organizations and individuals continually to potential security breaches. Technologies like artificial Intelligence, machine learning, and data modeling make it possible to design algorithms and neural networks that help anticipate events and generate more data. Subsequently, fling open the gates to potential data breaches. In essence, it becomes absolutely necessary to consider the aftermath of information security and privacy in this digital era.

The study addresses the necessity of informatics skills in the healthcare sector due to rapid technological advancements. It aims to establish a connection between skills taught in healthcare information technology (IT) courses and those required for health informatics jobs, offering solutions to bridge the skills gap. Job postings from Glassdoor, Indeed, and Monster in India from August to October 2022 were analyzed, yielding 926 initial posts. After refining, 44 jobs matched criteria. Details from postings, including organization nature, education, experience, skills, and software expertise, were collected. Analyst roles were common (53%), with IT service management systems prevailing (40%). Most jobs were full-time (98%) and mainly in Bengaluru (30%). Key skills were data modeling, data visualization, and Microsoft Office, along with programming languages like JAVA, Python, and SQL. Out of 77 colleges, only 18 met inclusion criteria. Recorded educational institution data encompassed healthcare IT management programs, course details, affiliations, and competency evaluation. In the evolving information society, creating a standardized health informatics curriculum is vital for India’s skilled workforce.

The conceptualization of cloud computing is the paradigm that has revolutionized software and hardware architecture across varied technological domains. However, governments and multiple sectors’ rapid adoption of cloud computing is a significant source of cost-saving, scalability, and collaboration mechanisms. Research in cloud computing technology and advancement is yet to fully embrace the complete spectrum of potential issues confronting cloud technology. It is the fastest-growing field that has gained global adoption in the IT space since 2007. Companies like Amazon, Google, Oracle, and Microsoft provide various products via cloud computing regardless of the many challenges they face in delivering their services to establish assurance to customers demanding continuity of services, speed to market, and guaranteed security. While there are multiple discussions around cloud and data safety, only a few have a grip on the dynamism of cloud technology and the background operation. Three major conventional institutions are at the forefront of providing a clear definition of cloud computing architecture, the technology innovation and advancement driving its operation. They are Gartner, Forrester, and the National Institute of Standards and Technology (NIST). The definition of cloud computing is presented differently by Gartner and Forrester, while the NIST explanation is based on industry-standard terms. This research is an in-depth look at cloud computing architecture from the three institutions’ perspectives. It provides researcher insight to uncover the background of cloud technology, tailored toward an extensive focus area for researchers and exposing a new stream of challenges that require a quick resolution.