Internet of Things (IoT) is widely accepted technology in both industrial as well as academic field. The objective of IoT is to combine the physical environment with the cyber world and create one big intelligent network. This technology has been applied to various application domains such as developing smart home, smart cities, healthcare applications, wireless sensor networks, cloud environment, enterprise network, web applications, and smart grid technologies. These wide emerging applications in variety of domains raise many security issues such as protecting devices and network, attacks in IoT networks, and managing resource-constrained IoT networks. To address the scalability and resource-constrained security issues, many security solutions have been proposed for IoT such as web application firewalls and intrusion detection systems. In this paper, a comprehensive survey on Intrusion Detection System (IDS) for IoT is presented for years 2015–2019. We have discussed various IDS placement strategies and IDS analysis strategies in IoT architecture. The paper discusses various intrusions in IoT, along with Machine Learning (ML) and Deep Learning (DL) techniques for detecting attacks in IoT networks. The paper also discusses security issues and challenges in IoT.

A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges

Cancer is one of the most feared and aggressive diseases in the world and is responsible for more than 9 million deaths universally. Staging cancer early increases the chances of recovery. One staging technique is RNA sequence analysis. Recent advances in the efficiency and accuracy of artificial intelligence techniques and optimization algorithms have facilitated the analysis of human genomics. This paper introduces a novel optimized deep learning approach based on binary particle swarm optimization with decision tree (BPSO-DT) and convolutional neural network (CNN) to classify different types of cancer based on tumor RNA sequence (RNA-Seq) gene expression data. The cancer types that will be investigated in this research are kidney renal clear cell carcinoma (KIRC), breast invasive carcinoma (BRCA), lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). The proposed approach consists of three phases. The first phase is preprocessing, which at first optimize the high-dimensional RNA-seq to select only optimal features using BPSO-DT and then, converts the optimized RNA-Seq to 2D images. The second phase is augmentation, which increases the original dataset of 2086 samples to be 5 times larger. The selection of the augmentations techniques was based achieving the least impact on manipulating the features of the images. This phase helps to overcome the overfitting problem and trains the model to achieve better accuracy. The third phase is deep CNN architecture. In this phase, an architecture of two main convolutional layers for featured extraction and two fully connected layers is introduced to classify the 5 different types of cancer according to the availability of images on the dataset. The results and the performance metrics such as recall, precision and F1 score show that the proposed approach achieved an overall testing accuracy of 96.90%. The comparative results are introduced, and the proposed method outperforms those in related works in terms of testing accuracy for 5 classes of cancer. Moreover, the proposed approach is less complex and consume less memory.

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Artificial Intelligence Technique for Gene Expression by Tumor RNA-Seq Data: A Novel Optimized Deep Learning Approach

Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.

Explainable artificial intelligence: a comprehensive review

Socially assistive robots can autonomously provide activity assistance to vulnerable populations, including those living with cognitive impairments. To provide effective assistance, these robots should be capable of displaying appropriate behaviors and personalizing them to a user's cognitive abilities. Our research focuses on the development of a novel robot learning architecture that uniquely combines learning from demonstration (LfD) and reinforcement learning (RL) algorithms to effectively teach socially assistive robots personalized behaviors. Caregivers can demonstrate a series of assistive behaviors for an activity to the robot, which it uses to learn general behaviors via LfD. This information is used to obtain initial assistive state-behavior pairings using a decision tree. Then, the robot uses an RL algorithm to obtain a policy for selecting the appropriate behavior personalized to the user's cognition level. Experiments were conducted with the socially assistive robot Casper to investigate the effectiveness of our proposed learning architecture. Results showed that Casper was able to learn personalized behaviors for the new assistive activity of tea-making, and that combining LfD and RL algorithms significantly reduces the time required for a robot to learn a new activity.

https://dl.acm.org/doi/pdf/10.1145/3277903

Learning and Personalizing Socially Assistive Robot Behaviors to Aid with Activities of Daily Living

Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. The study concluded that it may be used to build an automated system for the detection of severity of CKD.

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Chronic kidney disease diagnosis using decision tree algorithms.

Today the computer technology and computer network technology has developed so much and is still developing with pace.Thus, the amount of data in the information industry is getting higher day by day. This large amount of data can be helpful for analyzing and extracting useful knowledge from it. The hidden patterns of data are analyzed and then categorized into useful knowledge. This process is known as Data Mining. [4].Among the various data mining techniques, Decision Tree is also the popular one. Decision tree uses divide and conquer technique for the basic learning strategy. A decision tree is a flow chart-like structure in which each internal node represents a “test” on an attribute where each branch represents the outcome of the test and each leaf node represents a class label. This paper discusses various algorithms of the decision tree (ID3, C4.5, CART), their features, advantages, and disadvantages.

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Analysis of Various Decision Tree Algorithms for Classification in Data Mining

Urban traffic management is the system for assessing and controlling density of vehicles on road, to avoid traffic congestion. Vehicle counting is an important module it aids in traffic prediction and urban infrastructure planning. In this paper, various Artificial Intelligence tools and their applications for vehicle detection and counting have been analysed and elaborated. For vehicle detection, used deep learning based YOLOv3 algorithm a highly efficient and accurate algorithm for detecting different classes of vehicles. The role of edge-devices, which locally process the data that they are fed with. Application of Big Data and different AI tools like GNN, LSTM, and QCNN was described for vehicle detection and counting. AI is used to predict and detect traffic flow. Big data helps in maintaining an efficient traffic management system. Both combined, contribute in developing a systematic traffic management system. The objective behind using LSTM networks is to prevent the long term dependency problem. QCNN is used for efficient model training. Organizations implementing vehicle counting and detection focus more on the concise sensors and in house development of efficient algorithms in order to have extensive research and development.

Artificial Intelligence (AI) Enabled Vehicle Detection and counting Using Deep Learning

If You Don't Ask, You Don't Get It: Implementation of a Discrimination Screening Question Palliative Care Inpatient Consults (ODS5)

We Must Express Our Faith! The Impact of Religious Faith on Decision Making in Serious Illness and End-of-Life Care (SA304A)

In the product development and management area, .NET is critical. The sequential development of versions of .NET describes the importance and continuous feedback of customers about their experience. There are several architectural and functional differences of .NET evolution to its cross-platform version i.e., .NET core and above. Prominence of .NET in the improvement of development sector is evident. Quantum of open-source projects all over the globe and place of C# among the five most well-known programming languages are two pointers. Its ubiquity is simply going to develop, particularly now that the most recent emphasis (.NET 5) has changed business by presenting the idea of general programming advancement. .NET help for programming improvement isn’t restricted to the numerous programming dialects can utilize. .NET likewise advances utilization of a few prescribed procedures while allowing to utilize the methodology like to construct our application. .NET framework was the underlying kind of .NET. It gives engineer a bunch of APIs for most widely recognized programming needs and connects with basic working framework. It runs just on Windows, and its lifecycle is reaching a conclusion at this moment, after the arrival of .NET 5. Numerous executions emerged from that point forward, so the .NET name made ambiguities. .NET 5 means to make concrete the underlying vision of a widespread improvement stage.

Arpit Arora

Papers

Analysis of Various Decision Tree Algorithms for Classification in Data Mining

Artificial Intelligence (AI) Enabled Vehicle Detection and counting Using Deep Learning

If You Don't Ask, You Don't Get It: Implementation of a Discrimination Screening Question Palliative Care Inpatient Consults (ODS5)

We Must Express Our Faith! The Impact of Religious Faith on Decision Making in Serious Illness and End-of-Life Care (SA304A)

Architectural and Functional Differences in Dot Net Solutions