KARAN AGGARWAL (EIE)

The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes next generation IoT more vulnerable to privacy and security threats. With its “security by design,” Blockchain (BC) can help in addressing major security requirements in IoT. Blockchain is an ever-growing list of records that are linked and protected using cryptographic methods. It offers its users the flexibility to conduct transactions with lower costs and faster speeds. Blockchain ledgers are also decentralized and a ledger is maintained at each node in the network. Blockchain’s security and adaptability help in making even entire systems on it a much easily task with the benefit of decentralization. BC capabilities like immutability, transparency, auditability, data encryption, and operational resilience can help solve most architectural shortcomings of IoT. In the vision of the Internet of Things, tradit...

Generative Adversarial Network is the topic of interest in today’s research in the field of image processing and computer vision. A basic GAN model was introduced by Ian Goodfellow et al. in 2014. After that advancement in the field of research in GAN models has been application specific. In computer vision and image to image translation GANs are playing very effective role either in the case of face detection and recognition or in image resolution enhancement and image augmentation. This paper represents a concise overview of various GAN models along with their features and applications. Pix2Pix and conditional GAN models work upon paired datasets while other models like cycle GAN, discover GAN, dual GAN, info GAN, deep convolutional GAN etc. work upon unpaired datasets. Various image datasets which are commonly used for training of generator and discriminator networks are also discussed in this paper. Since partial mode collapse is a common problem to occur during training process...

The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which caused an inaccurate classification of toxic words in context. In this paper, several approaches for locating toxicity in texts are assessed and presented aiming to enhance the overall quality of text classification. General unsupervised methods were used depending on the state-of-art models and external embeddings to improve the accuracy while relieving bias and enhancing F1-score. Suggested approaches used a combination of long short-term memory (LSTM) deep learning model with Glove word embeddings and LSTM with word embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT), respectively. These models were trained and tested on large secondary qualitative dat...

In the modern era, artificial intelligence applications have become one of the most essential and prominent aspirations of countries in their various organisations and sectors, especially the education sector, due to the ability of these techniques to help this sector to develop rapidly and increase productivity by imparting scientific material in a beautiful way to the learners. This article provides an overview of the significance of artificial intelligence applications and their role in learning, and how they can be employed in the future. All information in this scenario is collected from a set of studies published in the time of COVID-19 pandemic between 2019 and 2021. This scenario concluded that artificial intelligence is the future that is constantly growing and can be benefited from in the field of education and must be properly exploited to build a new world that depends heavily on digital societies.

Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, subjective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, we present an automatic liver cirrhosis diagnosis system using ultrasound image matching. Given an input image, a preprocessing module is first performed to filter speckle noise and detect the extremely stable edge pattern which is defined by a similarity function using Hough transform. Furthermore, a template database is constructed to facilitate the detection of region of interests and liver cirrhosis classification. Then, support vector machine are employed to test a group of 30 in-vivo liver cirrhosis images from 18 patients, as well as other 30 liver images from 18 normal human volunteers. The results showed that the support vector machine was 92.4% in sensitivity for liver cirrhosis (LC) while ne...

Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, subjective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a method is proposed to extract the cirrhosis and normal liver features using the entropy of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, support vector machine are employed to test a group of 30 in-vivo liver cirrhosis images from 18 patients, as well as other 30 liver images from 18 normal human volunteers. The results showed that the support vector machine was 94.4% in sensitivity for Liver Cirrhosis (LC) while neural network provided 92.31 % in LC [23], and the system was considered to be helpful for clinical and educational use.

Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, subjective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a method is proposed to extract the cirrhosis and normal liver features using the entropy of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, support vector machine are employed to test a group of 30 in-vivo liver cirrhosis images from 18 patients, as well as other 30 liver images from 18 normal human volunteers. The results showed that the support vector machine is 94.4% in sensitivity for liver cirrhosis (LC) while neural network provided 92.31 % and the system is considered to be helpful for clinical and educational use.

All nations are facing significant challenges and difficulties in the face of an intense pandemic that controls human life, and many things have changed in life, which led to the doubling of the global economy and the difficulty of life for many of the world's inhabitants. At present, the nations are seeking to control the spread of the epidemic in them, as every country uses plans to prevent this pandemic. This article presents South Korea's experience in employing modern artificial intelligence techniques in facing COVID-19. Besides, the use of artificial intelligence techniques and their great role in analysing images of people with COVID-19 disease are discussed, and how they are used to save many lives of the victims. The data obtained in this article is from a group of studies, social networking sites, and news on TV channels in the first period of 2020, which are collected and summarised in this article. This study summarises the role of South Korea in facing the pand...

Today, humans fight powerful and active viruses that never take hold and do not know defeat, named coronaviruses. These viruses have start in 2002 and continued to grow and have changed their chains dramatically until now. They are known for having many similar features in common, and there are also structural differences between them. The most important reason that has turned coronaviruses into a pandemic is that this disease is easily transmitted by droplets near infected people, which leads to the spread of this virus faster worldwide. The more details known about coronaviruses that have profoundly affected humanity in the past and present and the diseases they cause, the more benefit in help designing an immune response or preventive vaccine to these viruses in the near future. In this article, coronaviruses, how they have been started and spread, and what differences and similarities are between them will be briefly covered here. The information of this investigation is taken f...