Internet of Things (IoT), Cloud, Big Data and AI-Machine Learning are topics of contemporary research interest. They
cover not only information and communication technology, but also all kinds of systems in our society, including business,
finance, industry, manufacture, management, and environment. IoT connects the physical world to the Internet and generates
big amount of data. Cloud computing environment facilitates the processing of large data and makes intelligent decisions based
on large data analyses and machine learning. This Thematic Special Issue on IoT, Cloud, Big Data and Machine Learning:
Recent Advances and Future Trends of the Journal International Journal of Sensors, Wireless Communications and Control
incorporate thirteen (13) articles identified with the field. A short review about the commitments for this Thematic Special
Issue is as follows:
1. P. Chandrashaker Reddy and A. Suresh Babu contribute an article entitled “An Enhanced Multiple Linear Regression Model
for Seasonal Rainfall Prediction”. The main goal of this project is early and proper rainfall forecasting, that helpful to people
who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in
their crop and water management using big data analytics which produces high in terms of profit and production for farmers.
In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model
(EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological
Department, Hyderabad) in 1901 to 2002 period.
2. Nguyen Thi Ngoc Anh, Nguyen Danh Tu, Vijender Kumar Solanki, Nguyen Linh Giang, Vu Hoai Thu, Luong Ngoc Son,
Nguyen Duc Loc, Vu Thanh Nam contribute an article entitled “Integrating Employee Value Model with Churn Prediction”.
The process of prediction integrating Churn, value of employee and machine learning are described detail in 6 steps. The
pros of integrating model gives the more necessary results for company than Churn prediction model but the cons is
complexity of model and algorithms and speed of computing. A case study of an organization with 1470 employee positions
is carried out to demonstrate the whole integrating churn predict, EVM and machine learning process. The accuracy of the
integrating model is high from 82% to 85%. Moreover, the some results of Churn and value employee are analyzed.
3. Charu Bhardwaj, Shruti Jain and Meenakshi Sood contribute an article entitled “Automated Diagnostic Hybrid Lesion
Detection System for Diabetic Retinopathy Abnormalities”. In this research paper, an automated lesion detection diagnostic
scheme has been proposed for early detection of retinal abnormalities of red and yellow pathological lesions. The algorithm
of the proposed Hybrid Lesion Detection (HLD) includes retinal image pre-processing, blood vessel extraction, optical disc
localization and detection stages for detecting the presence of diabetic retinopathy lesions. Automated diagnostic systems
assist the ophthalmologists practice manual lesion detection techniques which are tedious and time-consuming. Detailed
statistical analysis is performed on the extracted shape, intensity and GLCM features and the optimal features are selected to
classify DR abnormalities. Exhaustive statistical investigation of the proposed approach using visual and empirical analysis
resulted in 31 significant features. The results show that the HLD approach achieved good classification results in terms of
three statistical indices: accuracy, 98.9%; sensitivity, 97.8%; and specificity, 100% with significantly less complexity.
4. Anurag Satpathy, Ganapati Panda, Rajasekhar Gogula and Renu Sharma contribute an article entitled “Low Complexity
Adaptive Nonlinear Models for the Diagnosis of Periodontal Disease”. The paper addresses a specific clinical problem of
diagnosis of periodontal disease with an objective to develop and evaluate the performance of low complexity adaptive
nonlinear models (ANM) using nonlinear expansion schemes and describes the basic structure and development of ANMs in
detail. Diagnostic data pertaining to periodontal findings of teeth obtained from patients have been used as inputs to train and
validate the proposed models.
5. Sarat Chandra Nayak, Subhranginee Das and Mohd Dilsad Ansari contribute an article entitled “TLBO-FLN: Teachinglearning
Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction”. Stock closing price
prediction is enormously complicated. Artificial neural networks (ANN) are excellent approximation algorithms applied to
this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the
optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control
parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters
either leads toward additional computational cost or land at local optima. Teaching learning based optimization (TLBO) is a
newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of functional link
artificial neural network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data
made it popular and got wide applications to stock market prediction. This article presents a hybrid model termed as teaching
learning based optimization of functional neural networks (TLBO-FLN) by combining the advantages of both TLBO and
FLANN.
6. Sonal Agrawal and Pradeep Tripathi contribute an article entitled “Intuitionistic Fuzzy Score Function based Multi-Criteria
Decision Making Method for Selection of Cloud Service Provider”. Cloud computing (CC) has received great attention from
the scholarly researchers and IT companies. CC is a standard that offers services through the Internet. The standard has been
manipulated by existing skills (such as collect, peer-to-peer and grid computing) and currently accepted by approximately all
major associations. Various associations like as Microsoft and Facebook have revealed momentous investments in CC and
currently offer services with top levels of reliability. The well-organized and precise evaluation of cloud-based
communication network is an essential step in assurance both the business constancy and the continuous open services.
7. Syed Rameem Zahra and Mohammad Ahsan Chishti an article entitled “A Collaborative Edge-Cloud Internet of Things
based Framework for Securing the Indian Healthcare System”. Today, 73 years after the independence and twenty years after
the turn of the century, “Health for All” which should have been accomplished by now, remains a far-fetched and an elusive
dream. Instead, the people of India are bequeathed a triple burden of disease: sustaining the weight of transmittable
infections, expanding burden of nontransferable illnesses, and a healthcare system not efficient enough to handle them both.
At present, India is home to one-third of the poor population around the world. After a high population growth rate,
unregulated and inefficient healthcare is the major cause for this abjection and poverty. The global position of India vis-à-vis
the health indicators like Infant Mortality Rate (IMR), Crude Birth Rate (CBR), Crude Death Rate (CDR) and life
expectancy is shocking, shameful and on a downward trend. The objective of this paper was to identify the major issues in
the Indian healthcare system and offer Internet of Things (IoT) based solutions.
8. Tausifa Jan Saleem and Mohammad Ahsan Chishti contribute an article entitled “Exploring the Applications of Machine
Learning in Healthcare”. The objective of the research is to help the researchers in this field to get a comprehensive overview
of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this
paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.
9. Cerene Mariam Abraham, M. Sudheep Elayidom and T. Santhanakrishnan contribute an article entitled “Big Data Analysis
for Trend Recognition using Machine Learning Techniques”. This paper performs big data analytics on the Indian derivative
market and identifies a trend with the help of interdisciplinary areas such as cloud computing, machine learning and
statistical computing.
10. Mohammad Irfan Bala and Mohammad Ahsan Chishti contribute an article entitled “Comparative Analysis of Load
Balancing Algorithms for Cloud Computing in IoT”. This work focuses on multiple load balancing algorithms whose
performance has been analysed and compared under varying load conditions. Cloud computing is a widely adopted
computing paradigm and its importance has increased multi-folds in the recent past due to the inception of Internet of Things
(IoT).
11. Ravinder Ahuja, Vineet Maheshwari, Siddhant Manglik, Abiha Kazmi, Rishika Arora and Anuradha Gupta contribute an
article entitled “Malicious apps Identification in Android Devices Using Machine Learning Algorithms”. In this paper,
malicious apps detection system is implemented using machine learning algorithms. For this 330 permission based features
of 558 android applications are taken into consideration. The main motto of this work is to develop a model which can
effectively detect the malicious and benign apps. In this we have used six feature selection techniques which will extract
important features from 330 permission based features of 558 apps and further fourteen classification algorithms are applied
using Python language.
12. Mohammad Khalid Pandit, Roohie Naaz Mir and Mohammad Ahsan Chishti contribute an article entitled “Adaptive Deep
Neural Networks for the Internet of Things”. Deep neural networks have become the state of art technology for real world
classification tasks due to their ability to learn better feature representations at each layer. However, the added accuracy that
is associated with the deeper layers comes at a huge cost of computation, energy and added latency. The implementation of
such architectures in resource constraint IoT devices is computationally prohibitive due to its computational and memory
requirements. These factors are particularly severe in IoT domain. In this paper we propose adaptive deep neural network
(ADNN) which gets split across the compute hierarchical layers i.e. edge, fog and cloud with all splits having one or more
exit locations. At every location the data sample adaptively chooses to exit from the NN (based on confidence criteria) or get
fed into deeper layers housed across different compute layers.
13. Midde Veenkateswarlu Naik, D. Vasumathi and A.P. Siva Kumar contribute an article entitled “An Improved Intelligent
Approach to Enhance the Sentiment Classifier for Knowledge Discovery Using Machine Learning”. The objective of the
research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM)
based on parameter optimization is applied. The optimal feature selections to classify sentiment or opinion towards about
review documents have been determined with the help of particle swarm optimization approach. The proposed method
utilized three datasets to simulate the results such as airline sentiment data, weather sentiment data, and global warming data
that are freely available datasets.
We hope that the quality research work published in this special issue will be able to serve the concerned humanity, science
and technology.
