The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system.

/pdf/a-secure-iot-based-modern-healthcare-system-with-fault-49mpp19n20.pdf

A Secure IoT-Based Modern Healthcare System With Fault-Tolerant Decision Making Process

In medical field, previous patients’ cases are extremely private as well as intensely valuable to current disease diagnosis. Therefore, how to make full use of precious cases while not leaking out patients’ privacy is a leading and promising work especially in future privacy-preserving intelligent medical period. In this article, we investigate how to securely invoke patients’ records from past case-database while protecting the privacy of both current diagnosed patient and the case-database and construct a privacy-preserving medical record searching scheme based on ElGamal Blind Signature. In our scheme, by blinded the healthy data of the patient and the database of the iDoctor, respectively, the patient can securely make self-helped medical diagnosis by invoking past case-database and securely comparing the blinded abstracts of current data and previous records. Moreover, the patient can obtain target searching information intelligently at the same time he knows whether the abstracts match or not instead of obtaining it after matching. It greatly increases the timeliness of information acquisition and meets high-speed information sharing requirements, especially in 5G era. What's more, our proposed scheme achieves bilateral security, that is, whether the abstracts match or not, both of the privacy of the case-database and the private information of the current patient are well protected. Besides, it resists different levels of violent ergodic attacks by adjusting the number of zeros in a bit string according to different security requirements. 

<i>PMRSS:</i> Privacy-Preserving Medical Record Searching Scheme for Intelligent Diagnosis in IoT Healthcare

Human emotion recognition through artificial intelligence is one of the most popular research fields among researchers nowadays. The fields of Human Computer Interaction (HCI) and Affective Computing are being extensively used to sense human emotions. Humans generally use a lot of indirect and non-verbal means to convey their emotions. The presented exposition aims to provide an overall overview with the analysis of all the noteworthy emotion detection methods at a single location. To the best of our knowledge, this is the first attempt to outline all the emotion recognition models developed in the last decade. The paper is comprehended by expending more than hundred papers; a detailed analysis of the methodologies along with the datasets is carried out in the paper. The study revealed that emotion detection is predominantly carried out through four major methods, namely, facial expression recognition, physiological signals recognition, speech signals variation and text semantics on standard databases such as JAFFE, CK+, Berlin Emotional Database, SAVEE, etc. as well as self-generated databases. Generally seven basic emotions are recognized through these methods. Further, we have compared different methods employed for emotion detection in humans. The best results were obtained by using Stationary Wavelet Transform for Facial Emotion Recognition , Particle Swarm Optimization assisted Biogeography based optimization algorithms for emotion recognition through speech, Statistical features coupled with different methods for physiological signals, Rough set theory coupled with SVM for text semantics with respective accuracies of 98.83%,99.47%, 87.15%,87.02% . Overall, the method of Particle Swarm Optimization assisted Biogeography based optimization algorithms with an accuracy of 99.47% on BES dataset gave the best results.

https://iecscience.org/uploads/jpapers/202003/dnQToaqdF8IRjhE62pfIovCkDJ2jXAcZdK6KHRzM.pdf

Emotion Recognition and Detection Methods: A Comprehensive Survey

Security is a priority in information system, especially in the exchange of data that are important or confidential. The information to be given to the party entitled to the information must be properly safeguarded, don’t fall into the other hands who have no right to such information. One way to maintain the security of information exchanged in a system can be done using cryptographic techniques. Cryptography is the art and science to hide information from third parties. In cryptography a person who has a private key can convert plaintext data into unique and unreadable data (ciphertext) and can convert existing ciphertext into plaintext form by using its private key. System development (System Development Lifecycle) can mean to construct a new system to replace old system, combined with prototyping technique to build a cryptographic system using Nur algorithm which is implemented using programming language used is assembly (MASM32). In Nur Aminuddin's Encryptor there are two data-reading techniques namely encryption technique (the technique of converting data from the original into unreadable code) and decryption technique (the techniques of reading unreadable codes become readable) Encryption technique is built by applying technique of modern cryptography which holds secrecy on the symmetric key, so the security of encryption depends only on the key and does not depend on whether the algorithm is known to people or not.

Nur algorithm on data encryption and decryption

The purpose of this article is to identify blockchain's role in achieving logistic objectives, with the aim of pointing out the significance of blockchain technology in the digital transformation of logistics and transportation. Implementation of the blockchain technology in combination with IoT elements in logistics and transportation contributes to business process optimization, supply chain traceability and transparency, and significant financial savings. There are limitations as blockchain is at a relatively early stage of development with most projects. Therefore, the approach of theory building from multiple case studies was used. The article provides a comprehensive overview of current blockchain initiatives and use-cases. This article is believed to be the first to address the role of blockchain technology in achieving logistics objectives. Results from this research indicate that blockchain technology contributes to the achievement of logistics objectives.

The Significance of Blockchain Technology in Digital Transformation of Logistics and Transportation

Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.

/pdf/opinion-mining-on-newspaper-headlines-using-svm-and-nlp-37cl95c8c3.pdf

Opinion mining on newspaper headlines using SVM and NLP

Human computer interaction will be natural and effective when the interfaces are sensitive to human emotion or stress. Previous studies were mainly focused on facial emotion recognition but speech emotion detection is gaining importance due its wide range of applications. Speech emotion recognition still remains a challenging task in the field of affective computing as no defined standards exist for emotion classification. Speech signal carries large information related to the emotions conveyed by a person. Speech recognition system fails miserably if robust techniques are not implemented to address the variations in speech due to emotion. Emotion detection from speech has two main steps. They are feature extraction and classification. The goal of this paper is to give an overview on the types of corpus, features and classification techniques that are associated with speech emotion recognition.

A comprehensive survey on features and methods for speech emotion detection

Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.

/pdf/nlp-based-personal-learning-assistant-for-school-education-lzir67rg8h.pdf

NLP-based personal learning assistant for school education

Remote health monitoring can offer a lot of advantage to all the players in healthcare industry and it can contribute to reduced healthcare expenses. Wireless medical sensor networks capable of accumulating and transferring vital parameters of patients play a crucial role in remote health monitoring. Security and privacy are major concerns preventing the patients from adopting this technology with an open mind. This paper presents a signature-based authentication protocol for remote health monitoring. The work also discusses an authentication protocol for the mutual authentication of users and medical server. The protocol does not require the server to maintain a password table. The proposed algorithms are resistant to various attacks such as replay attack, stolen verifier attack, and privileged insider attack. The work includes the informal and formal security analysis of the proposed protocols. Scyther tool is used for formal security analysis and the results show that the protocol is resistant to various common and automated attacks.

https://link.springer.com/content/pdf/10.1007/s42979-019-0010-3.pdf

A Signature-Based Mutual Authentication Protocol for Remote Health Monitoring

Managing a corporate network has always been a challenge. The network consists of a variety of devices right from routers and switches to the firewalls. Centrally controlled Software Defined Networks has always been proposed as a solution to the current limitations of the traditional networks as it redefines the network. It has overcome the traditional networking limitations by separating the data and control plane. A new modular programming language is introduced that focuses on specifying the network policies for high-level abstractions that are embedded and implemented using Python on Software Defined Networks. In this paper, the authors throws light on the Frenetic, one of the high-level programming language which assess and implement the Pyretic policies to evaluate the performance of Software Defined Network by implementing and observing the scalability aspect of the futuristic SDN corporate networks. The experiment is being performed over the various topologies which range from 100 to 1000 nodes and further analyzing the Pyretic behaviour based on the obtained experimental results to demonstrate the scalability aspect of SDN based corporate networks. A diverse set of networking topologies are experimented by emulating SDN using Mininet and Iperf.

Joy Paulose

Papers

Opinion mining on newspaper headlines using SVM and NLP

A comprehensive survey on features and methods for speech emotion detection

NLP-based personal learning assistant for school education

A Signature-Based Mutual Authentication Protocol for Remote Health Monitoring

Experimenting with Scalability of Software Defined Networks Using Pyretic and Frenetic