Efficient Tumor Detection in MRI Brain Images

Detection of brain of tumor is a laborious task as it involves identification, segmentation followed by detection of the tumor. It is a very challenging task to envisage uncommon structures in the image of human brain[15].    An Image processing concept called MRI can be used to visualize different structures of human body. The Magnetic Resonance images (MRI) are used to detect the uncommon portions of human brain. This paper explores different noise removal methods accompanied by Balance-contrast enhancement technique (BCET) which results in increased accuracy. Segmentation followed by canny edge detection is performed on the improved images to detect the fine edges of the abnormalities present. The model attained an accuracy of at most 98% in detecting the tumor or the abnormality in a human brain which determines the effectiveness of the proposed model

Efficient Tumor Detection in MRI Brain Images

Detection of brain of tumor is a laborious task as it involves identification, segmentation followed by detection of the tumor. It is a very challenging task to envisage uncommon structures in the image of human brain[15].    An Image processing concept called MRI can be used to visualize different structures of human body. The Magnetic Resonance images (MRI) are used to detect the uncommon portions of human brain. This paper explores different noise removal methods accompanied by Balance-contrast enhancement technique (BCET) which results in increased accuracy. Segmentation followed by canny edge detection is performed on the improved images to detect the fine edges of the abnormalities present. The model attained an accuracy of at most 98% in detecting the tumor or the abnormality in a human brain which determines the effectiveness of the proposed model.</span

Neural Network Modelling of Speech Emotion Detection

In making the Machines Intelligent, and enable them to work as human, Speech recognition is one of the most essential requirement. Human Language conveys various types of information such as the energy, pitch, loudness, rhythm etc., in the sound, the speech and its context such as gender, age and the emotion. Identifying the emotion from a speech pattern is a challenging task and the most useful solution especially in the era of widely developing speech recognition systems with digital assistants. Digital assistants like Bixby, Blackberry assistant are building products that consist of emotion identification and reply the user in step with user point of view. The objective of this work is to improve the accuracy of the speech emotion prediction using deep learning models. Our work experiments with the MLP and CNN classification models on three benchmark datasets with 5700 speech files of 7 emotion categories. The proposed model showed improved accuracy

Natural Language to SQL: Automated Query Formation Using NLP Techniques

In this era of information world, given any topic, we are able to get relevant data or documents at a mouse click. The flexibility that internet provides is the user friendly language or Natural Language to search for required topic. Natural Language Querying and Retrieval has made internet popular. It is implicit for business user to understand what the business data is indicating to find better business opportunities. Querying for required data the business users are using SQL. To effectively Query such systems, the Business users has to master the Language. But many business users may not be aware of the SQL language or may not be aware of the databases and some users feel difficulty to write the long SQL Queries. Therefore, it is equally important to query the database very easily. The work here presents a case study to help the business users to type a query in Natural Language, which then converts into SQL statement and process this SQL query against the Databases and get the expected result. This work proposes QCNER approach to extract SQL properties from Natural Language Query. The proposed approach after the application of SMOTE technique depicts 92.31 accuracy over the existing models.

AI enabled legal assistance system: A case study

Given a Case, finding the related prior cases and their judgements is a time consuming job of a Lawyer. The lawyer has to go through huge volumes of law books and prepare his case.&nbsp; An automated tool that retrieves the relevant past cases and their judgments is a very useful application for lawyers.&nbsp; It is a complex task especially in Indian context, the cases and their judgments are un-structured, and there is no standard format of case and judgement presentation.&nbsp; It is understandable for a lawyer but, a most difficult task for a machine. The work here presents a case study to retrieve judgments given in the past for a given factual description. The dataset considered for this work is selected from FIRE-2019, AILA track.&nbsp; The previously developed models showed best average precision of 0.149 using BM25, which itself demonstrates the challenging aspect of the given task.&nbsp;&nbsp; In this work LDA, a probabilistic algorithm for Topic Modelling is explored and studied. The proposed method has shown improved precision

Sustainably empowered crowdfunding through blockchain-enhanced security technology

A sustainable crowdfunding smart contract is a specialized type of smart contract that empowers individuals to contribute funds towards a specific work or venture while ensuring ecological and ethical longevity. The smart contract is meticulously programmed to establish the parameters of the crowdfunding campaign, encompassing the total funds requisite for project completion, the quantity of available tokens for acquisition, campaign duration, and token valuation. In alignment with sustainable principles, the contract orchestrates the transparent collection and distribution of funds. It unequivocally designates the blockchain address for fund allocation and delineates how these funds will be equitably apportioned among the project’s development team, thus promoting sustainable practices.Furthermore, the sustainable crowdfunding smart contract features a built-in mechanism to restore funds to contributors in the event that the project fails to achieve its funding target within the stipulated time frame. Upon campaign culmination, the smart contract autonomously dispenses tokens to contributors and allocates funds to the project’s development team, fostering an environment of trust and accountability. This sustainable approach leverages the power of smart contracts to streamline and automate the entire process, assuring contributors that their resources will be judiciously utilized for their intended purpose. A pivotal advantage of this innovative approach lies in the elimination of intermediaries, fostering heightened security for assets through a human-intervention-free programmed process. By combining blockchain technology as the foundational backend and leveraging solidity for smart contract implementation, this sustainable solution exemplifies the pinnacle of accuracy and reliability. Through this seamless integration of technology and sustainability, the paper presents a progressive paradigm in crowdfunding that resonates with environmentally conscious and ethically mindful principles

Conversational AI Chatbot for HealthCare

Health is a state of total physical, mental, and social wellbeing. chatbots have been applied to this industry frequently and in a variety ofways in the past, there is still room for more inventive uses. Healthcareconversational AI use cases are flexible and may be tailored to the industry. Patients might use them to gain additional knowledge about their disease, the therapies that are available, or even their insurance coverage. Because research has shown that healthcare chatbots can improve patient satisfaction and significantly reduce wait times, many healthcare organisations are considering incorporating them into their operations. Chatbots for healthcare can be used for a number of purposes, such as monitoring, anonymity, personalization, in-person involvement, and more. In this case study, the user's input on the patient's symptoms will be used to determine the patient's likely ailment type. According on the type of sickness, precautions will be suggested, and the patient will be sent to a doctor who specialises in that field. A sequential model was utilised to extract the text's symptoms, and the KNN method was then applied to predict the patient's ailment type.