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Dr. AKASH KUMAR BHOI
Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University (SMU)

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0 Healthcare technologies
0 Biomedial engineering
0 machine learning

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Short Biography

AKASH KUMAR BHOI [B.Tech, M.Tech, Ph.D.] is working as Assistant Professor (Research) in the Department of Electrical and Electronics Engineering at Sikkim Manipal Institute of Technology (SMIT), India, since 2012. He is also working (Period: 20th Jan 2021 - 19th Jan 2022) as Research Associate at Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) Pisa, Italy. He is a University Ph.D. Course Coordinator for “Research & Publication Ethics (RPE).” He is a member of IEEE, ISEIS, and IAENG, an associate member of IEI, UACEE, and an editorial board member reviewer of Indian and international journals. He is also a regular reviewer of repute journals, namely IEEE, Springer, Elsevier, Taylor and Francis, Inderscience, etc. His research areas are Biomedical Technologies, the Internet of Things, Computational Intelligence, Antenna, Renewable Energy. He has published several papers in national and international journals and conferences. He has 100+ documents registered in the Scopus database by the year 2020. He has also served on numerous organizing panels for international conferences and workshops. He is currently editing several books with Springer Nature, Elsevier and Routledge & CRC Press. He is also serving as Guest editor for special issues of the journal like Springer Nature and Inderscience.

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Journal article
Published: 06 July 2021 in Biomedical Signal Processing and Control
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Sleep staging is an important part of diagnosing the different types of sleep-related disorders because any discrepancies in the sleep scoring process may cause serious health problems such as misinterpretations of sleep patterns, medication errors, and improper diagnosis. The best way of analyzing sleep staging is visual interpretations of the polysomnography (PSG) signals recordings from the patients, which is a quite tedious task, requires more domain experts, and time-consuming process. This proposed study aims to develop a new automated sleep staging system using the brain EEG signals. Based on a new automated sleep staging system based on an ensemble learning stacking model that integrates Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting). Additionally, this proposed methodology considers the subjects’ age, which helps analyze the S1 sleep stage properly. In this study, both linear (time and frequency) and non-linear features are extracted from the pre-processed signals. The most relevant features are selected using the ReliefF weight algorithm. Finally, the selected features are classified through the proposed two-layer stacking model. The proposed methodology performance is evaluated using the two most popular datasets, such as the Sleep-EDF dataset (S-EDF) and Sleep Expanded-EDF database (SE-EDF) under the Rechtschaffen & Kales (R&K) sleep scoring rules. The performance of the proposed method is also compared with the existing published sleep staging methods. The comparison results signify that the proposed sleep staging system has an excellent improvement in classification accuracy for the six-two sleep states classification. In the S-EDF dataset, the overall accuracy and Cohen’s kappa coefficient score obtained by the proposed model is (91.10%, 0.87) and (90.68%, 0.86) with inclusion and exclusion of age feature using the Fpz-Cz channel, respectively. Similarly, the Pz-Oz channel’s performance is (90.56%, 0.86) with age feature and (90.11%, 0.86) without age feature. The performed results with the SE-EDF dataset using Fpz-Cz channel is (81.32%, 0.77) and (81.06%, 0.76), using Pz-Oz channel with the inclusion and exclusion of the age feature, respectively. Similarly the model achieved an overall accuracy of 96.67% (CT-6), 96.60% (CT-5), 96.28% (CT-4),96.30% (CT-3) and 97.30% (CT-2) for with 16 selected features using S-EDF database. Similarly the model reported an overall accuracy of 85.85%, 84.98%, 85.51%, 85.37% and 87.40% for CT-6 to CT-2 with 18 selected features using SE-EDF database.

ACS Style

Santosh Kumar Satapathy; Akash Kumar Bhoi; D. Loganathan; Bidita Khandelwal; Paolo Barsocchi. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal. Biomedical Signal Processing and Control 2021, 69, 102898 .

AMA Style

Santosh Kumar Satapathy, Akash Kumar Bhoi, D. Loganathan, Bidita Khandelwal, Paolo Barsocchi. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal. Biomedical Signal Processing and Control. 2021; 69 ():102898.

Chicago/Turabian Style

Santosh Kumar Satapathy; Akash Kumar Bhoi; D. Loganathan; Bidita Khandelwal; Paolo Barsocchi. 2021. "Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal." Biomedical Signal Processing and Control 69, no. : 102898.

Review
Published: 29 June 2021 in Energies
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Energy consumption is a crucial domain in energy system management. Recently, it was observed that there has been a rapid rise in the consumption of energy throughout the world. Thus, almost every nation devises its strategies and models to limit energy usage in various areas, ranging from large buildings to industrial firms and vehicles. With technological advancements, computational intelligence models have been successfully contributing to the prediction of the consumption of energy. Machine learning and deep learning-based models enhance the precision and robustness compared to traditional approaches, making it more reliable. This article performs a review analysis of the various computational intelligence approaches currently being utilized to predict energy consumption. An extensive survey procedure is conducted and presented in this study, and relevant works are discussed. Different criteria are considered during the aggregation of the relevant studies relating to the work. The author’s perspective, future trends and various novel approaches are also presented as a part of the discussion. This article thereby lays a foundation stone for further research works to be undertaken for energy prediction.

ACS Style

Sunil Mohapatra; Sushruta Mishra; Hrudaya Tripathy; Akash Bhoi; Paolo Barsocchi. A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches. Energies 2021, 14, 3900 .

AMA Style

Sunil Mohapatra, Sushruta Mishra, Hrudaya Tripathy, Akash Bhoi, Paolo Barsocchi. A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches. Energies. 2021; 14 (13):3900.

Chicago/Turabian Style

Sunil Mohapatra; Sushruta Mishra; Hrudaya Tripathy; Akash Bhoi; Paolo Barsocchi. 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches." Energies 14, no. 13: 3900.

Article
Published: 16 June 2021 in Sādhanā
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Chalcogenide Glasses are of recent interest in the field of chemical and bio-sensing applications. These are considered either in stored memory devices or in optical domain. The involvement of such material in the bio-sensing application for the development of SPR (Surface Plasmon Resonance) Sensors is very essential and thus, has been considered in this manuscript. In a Kretschmann SPR configuration, a thin-film layer of such Chalcogenide material has been incorporated which is further accompanied with a Graphene layer. The optical properties of both the materials viz. chalcogenide and graphene have been portrayed and presented in this paper by taking the assistance of MATLAB environment and Characteristic Transfer Matrix (CTM) Method.

ACS Style

Jitendra Singh Tamang; Rudra Sankar Dhar; Akash Kumar Bhoi; Arun Kumar Singh; Somenath Chatterjee. Bio-sensing application of chalcogenide thin film in a graphene-based surface plasmon resonance (SPR) sensor. Sādhanā 2021, 46, 1 -10.

AMA Style

Jitendra Singh Tamang, Rudra Sankar Dhar, Akash Kumar Bhoi, Arun Kumar Singh, Somenath Chatterjee. Bio-sensing application of chalcogenide thin film in a graphene-based surface plasmon resonance (SPR) sensor. Sādhanā. 2021; 46 (3):1-10.

Chicago/Turabian Style

Jitendra Singh Tamang; Rudra Sankar Dhar; Akash Kumar Bhoi; Arun Kumar Singh; Somenath Chatterjee. 2021. "Bio-sensing application of chalcogenide thin film in a graphene-based surface plasmon resonance (SPR) sensor." Sādhanā 46, no. 3: 1-10.

Journal article
Published: 15 June 2021 in Electronics
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In the contemporary world, with ever-evolving internet models in the process of automating and digitalizing various industrial and domestic implementations, the Internet of Things (IoT) has made remarkable advancements in sharing the healthcare data and triggering the associated necessary actions. Healthcare-related data sharing among the intermediate nodes, privacy, and data integrity are the two critical challenges in the present-day scenario. Data needs to be encrypted to ensure the confidentiality of the sensitive information shared among the nodes, especially in the case of healthcare-related data records. Implementing the conventional encryption algorithms over the intermediate node may not be technically feasible, and too much burden on the intermediate nodes is not advisable. This article has focused on various security challenges in the existing mechanism, existing strategies in security solutions for IoT driven healthcare monitoring frameworks and proposes a context-aware state of art model based on Blockchain technology that has been deployed for encrypting the data among the nodes in the architecture of a 5G network. The proposed strategy was examined through various performance evaluation metrics, and the proposed approach had outperformed compared to its counterparts.

ACS Style

Parvathaneni Srinivasu; Akash Bhoi; Soumya Nayak; Muhammad Bhutta; Marcin Woźniak. Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network. Electronics 2021, 10, 1437 .

AMA Style

Parvathaneni Srinivasu, Akash Bhoi, Soumya Nayak, Muhammad Bhutta, Marcin Woźniak. Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network. Electronics. 2021; 10 (12):1437.

Chicago/Turabian Style

Parvathaneni Srinivasu; Akash Bhoi; Soumya Nayak; Muhammad Bhutta; Marcin Woźniak. 2021. "Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network." Electronics 10, no. 12: 1437.

Review
Published: 09 May 2021 in Sustainability
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Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.

ACS Style

Lewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability 2021, 13, 5279 .

AMA Style

Lewlisa Saha, Hrudaya Tripathy, Soumya Nayak, Akash Bhoi, Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability. 2021; 13 (9):5279.

Chicago/Turabian Style

Lewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review." Sustainability 13, no. 9: 5279.

Journal article
Published: 18 April 2021 in Sensors
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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

ACS Style

Parvathaneni Srinivasu; Jalluri SivaSai; Muhammad Ijaz; Akash Bhoi; Wonjoon Kim; James Kang. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors 2021, 21, 2852 .

AMA Style

Parvathaneni Srinivasu, Jalluri SivaSai, Muhammad Ijaz, Akash Bhoi, Wonjoon Kim, James Kang. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors. 2021; 21 (8):2852.

Chicago/Turabian Style

Parvathaneni Srinivasu; Jalluri SivaSai; Muhammad Ijaz; Akash Bhoi; Wonjoon Kim; James Kang. 2021. "Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM." Sensors 21, no. 8: 2852.

Journal article
Published: 31 March 2021 in Mathematics
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The widespread acceptance and increase of the Internet and mobile technologies have revolutionized our existence. On the other hand, the world is witnessing and suffering due to technologically aided crime methods. These threats, including but not limited to hacking and intrusions and are the main concern for security experts. Nevertheless, the challenges facing effective intrusion detection methods continue closely associated with the researcher’s interests. This paper’s main contribution is to present a host-based intrusion detection system using a C4.5-based detector on top of the popular Consolidated Tree Construction (CTC) algorithm, which works efficiently in the presence of class-imbalanced data. An improved version of the random sampling mechanism called Supervised Relative Random Sampling (SRRS) has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage. Moreover, an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection. The proposed IDS has been validated with state-of-the-art intrusion detection systems. The results show an accuracy of 99.96% and 99.95%, considering the NSL-KDD dataset and the CICIDS2017 dataset using 34 features.

ACS Style

Ranjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Yogesh Kumar; Rutvij Jhaveri. A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets. Mathematics 2021, 9, 751 .

AMA Style

Ranjit Panigrahi, Samarjeet Borah, Akash Bhoi, Muhammad Ijaz, Moumita Pramanik, Yogesh Kumar, Rutvij Jhaveri. A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets. Mathematics. 2021; 9 (7):751.

Chicago/Turabian Style

Ranjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Yogesh Kumar; Rutvij Jhaveri. 2021. "A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets." Mathematics 9, no. 7: 751.

Review
Published: 23 March 2021 in Mathematics
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Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

ACS Style

Ranjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Rutvij Jhaveri; Chiranji Chowdhary. Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research. Mathematics 2021, 9, 690 .

AMA Style

Ranjit Panigrahi, Samarjeet Borah, Akash Bhoi, Muhammad Ijaz, Moumita Pramanik, Rutvij Jhaveri, Chiranji Chowdhary. Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research. Mathematics. 2021; 9 (6):690.

Chicago/Turabian Style

Ranjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Rutvij Jhaveri; Chiranji Chowdhary. 2021. "Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research." Mathematics 9, no. 6: 690.

Journal article
Published: 15 March 2021 in IRBM
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The initial principal task of a Brain-Computer Interfacing (BCI) research is to extract the best feature set from a raw EEG (Electroencephalogram) signal so that it can be used for the classification of two or multiple different events. The main goal of the paper is to develop a comparative analysis among different feature extraction techniques and classification algorithms. In this present investigation, four different methodologies have been adopted to classify the recorded MI (motor imagery) EEG signal, and their comparative study has been reported. Haar Wavelet Energy (HWE), Band Power, Cross-correlation, and Spectral Entropy (SE) based Cross-correlation feature extraction techniques have been considered to obtain the necessary features set from the raw EEG signals. Four different machine learning algorithms, viz. LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis), Naïve Bayes, and Decision Tree, have been used to classify the features. The best average classification accuracies are 92.50%, 93.12%, 72.26%, and 98.71% using the four methods. Further, these results have been compared with some recent existing methods. The comparative results indicate a significant accuracy level performance improvement of the proposed methods with respect to the existing one. Hence, this presented work can guide to select the best feature extraction method and the classifier algorithm for MI-based EEG signals.

ACS Style

G. Roy; A.K. Bhoi; S. Bhaumik. A Comparative Approach for MI-Based EEG Signals Classification Using Energy, Power and Entropy. IRBM 2021, 1 .

AMA Style

G. Roy, A.K. Bhoi, S. Bhaumik. A Comparative Approach for MI-Based EEG Signals Classification Using Energy, Power and Entropy. IRBM. 2021; ():1.

Chicago/Turabian Style

G. Roy; A.K. Bhoi; S. Bhaumik. 2021. "A Comparative Approach for MI-Based EEG Signals Classification Using Energy, Power and Entropy." IRBM , no. : 1.

Original research paper
Published: 08 February 2021 in IET Generation, Transmission & Distribution
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Conventional transmission line protection algorithms may experience delay or even mal‐operation in presence of shunt compensation due to various non‐fault transient disturbances. This paper presents a time‐domain differential protection technique based on Kullback–Leibler divergence with thresholding logic for mid‐point static synchronous compensator compensated transmission system. A detection index for each phase currents are computed in order to discrimination the fault and non‐fault scenarios. Different structure of power systems modelled using EMTDC/PSCAD are simulated to generate numerous numbers of test cases in order to evaluate the applicability of the proposed differential protection logic. Proposed method is also efficient to differentiate the faults during various non‐fault events like capacitor switching, sudden load change, power swing, and current transformer saturation including external fault cases. Results and comparative assessment with recently proposed time‐frequency and commercial current differential relaying reports demonstrate the efficacy and robustness of the proposed technique. The results confirmed the accurate operation of the proposed protection technique.

ACS Style

Sandeep Biswal; Mohsen Tajdinian; Ahmed. R. Adly; Ram Dayal Patidar; Akash Kumar Bhoi; Natwar Singh Rathore. Kullback‐Leibler divergence based differential protection scheme for shunt compensated transmission line. IET Generation, Transmission & Distribution 2021, 15, 1808 -1819.

AMA Style

Sandeep Biswal, Mohsen Tajdinian, Ahmed. R. Adly, Ram Dayal Patidar, Akash Kumar Bhoi, Natwar Singh Rathore. Kullback‐Leibler divergence based differential protection scheme for shunt compensated transmission line. IET Generation, Transmission & Distribution. 2021; 15 (12):1808-1819.

Chicago/Turabian Style

Sandeep Biswal; Mohsen Tajdinian; Ahmed. R. Adly; Ram Dayal Patidar; Akash Kumar Bhoi; Natwar Singh Rathore. 2021. "Kullback‐Leibler divergence based differential protection scheme for shunt compensated transmission line." IET Generation, Transmission & Distribution 15, no. 12: 1808-1819.

Conference paper
Published: 29 January 2021 in Lecture Notes in Electrical Engineering
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Internet of things (IoT) concept is related to a ubiquitous and pervasive connection of cyber-physical systems to the Internet. These cyber-physical systems can be identified and incorporate communication and sensing capabilities which turn them able to cooperate in a collective objective. IoT architectures have been successfully tested and valeted in several fields. Food safety is a relevant topic for public health and well-being. This paper presents a literature review on the application of IoT architectures for food monitoring in the past five years (2014–2019). The main contribution is to synthesize the existing body of knowledge, to identify common threads and gaps that would open up new challenging, relevant, and significant research directions. The review on real applications of IoT for food quality monitoring has conducted by analysed 13 studies which show that IoT implementation in this field is rare. The results state that most of the IoT implementations have been conducted in Asia, particularly by Indian authors. The most used sensors in these systems are temperature, humidity and gas sensors, and the most used communication technologies are ZigBee, Wi-Fi, radio-frequency identification (RFID), and Bluetooth low energy (BLE). Furthermore, the authors found exceptional potential in the implementation of IoT for food monitoring systems; however, some limitations are also found.

ACS Style

Raquel Margarida Dias; Gonçalo Marques; Akash Kumar Bhoi. Internet of Things for Enhanced Food Safety and Quality Assurance: A Literature Review. Lecture Notes in Electrical Engineering 2021, 653 -663.

AMA Style

Raquel Margarida Dias, Gonçalo Marques, Akash Kumar Bhoi. Internet of Things for Enhanced Food Safety and Quality Assurance: A Literature Review. Lecture Notes in Electrical Engineering. 2021; ():653-663.

Chicago/Turabian Style

Raquel Margarida Dias; Gonçalo Marques; Akash Kumar Bhoi. 2021. "Internet of Things for Enhanced Food Safety and Quality Assurance: A Literature Review." Lecture Notes in Electrical Engineering , no. : 653-663.

Journal article
Published: 08 January 2021 in Applied Sciences
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Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson’s detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson’s Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson’s and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson’s disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%.

ACS Style

Moumita Pramanik; Ratika Pradhan; Parvati Nandy; Akash Kumar Bhoi; Paolo Barsocchi. Machine Learning Methods with Decision Forests for Parkinson’s Detection. Applied Sciences 2021, 11, 581 .

AMA Style

Moumita Pramanik, Ratika Pradhan, Parvati Nandy, Akash Kumar Bhoi, Paolo Barsocchi. Machine Learning Methods with Decision Forests for Parkinson’s Detection. Applied Sciences. 2021; 11 (2):581.

Chicago/Turabian Style

Moumita Pramanik; Ratika Pradhan; Parvati Nandy; Akash Kumar Bhoi; Paolo Barsocchi. 2021. "Machine Learning Methods with Decision Forests for Parkinson’s Detection." Applied Sciences 11, no. 2: 581.

Hypothesis
Published: 17 November 2020 in Applied Sciences
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There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.

ACS Style

Sushruta Mishra; Pradeep Mallick; Hrudaya Tripathy; Akash Bhoi; Alfonso González-Briones. Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier. Applied Sciences 2020, 10, 8137 .

AMA Style

Sushruta Mishra, Pradeep Mallick, Hrudaya Tripathy, Akash Bhoi, Alfonso González-Briones. Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier. Applied Sciences. 2020; 10 (22):8137.

Chicago/Turabian Style

Sushruta Mishra; Pradeep Mallick; Hrudaya Tripathy; Akash Bhoi; Alfonso González-Briones. 2020. "Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier." Applied Sciences 10, no. 22: 8137.

Journal article
Published: 23 August 2020 in Electronics
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In this manuscript, an antenna on textile (jeans) substrate is presented for the WLAN, C band and X/Ku band. This is a wearable textile antenna, which was formed on jeans fabric substrate to reduce surface-wave losses. The proposed antenna design consists of a patch and a defected ground. To energize the wearable textile antenna, a microstrip line feed technique is used in the design. The impedance band width of 23.37% (3.4–4.3 GHz), 56.48% (4.7–8.4 GHz) and 31.14% (10.3–14.1 GHz) frequency bands are observed, respectively. The axial ratio bandwidth (ARBW) of 10.10% (4.7–5.2 GHz), 4.95% (5.9–6.2 GHz) and 10.44% (11.8–13.1 GHz) frequency bands are observed, respectively. A peak gain of 4.85 dBi is analyzed at 4.1-GHz frequency during the measurement. The SAR value was calculated to observe the radiation effect and it was found that its utmost SAR value is 1.8418 W/kg and 1.919 W/kg at 5.2/5.5-GHz frequencies, which is less than 2 W/kg of 10 gm tissue. The parametric study is performed for the validation of the proper functioning of the antenna.

ACS Style

Ashok Yadav; Vinod Kumar Singh; Pranay Yadav; Amit Kumar Beliya; Akash Kumar Bhoi; Paolo Barsocchi. Design of Circularly Polarized Triple-Band Wearable Textile Antenna with Safe Low SAR for Human Health. Electronics 2020, 9, 1366 .

AMA Style

Ashok Yadav, Vinod Kumar Singh, Pranay Yadav, Amit Kumar Beliya, Akash Kumar Bhoi, Paolo Barsocchi. Design of Circularly Polarized Triple-Band Wearable Textile Antenna with Safe Low SAR for Human Health. Electronics. 2020; 9 (9):1366.

Chicago/Turabian Style

Ashok Yadav; Vinod Kumar Singh; Pranay Yadav; Amit Kumar Beliya; Akash Kumar Bhoi; Paolo Barsocchi. 2020. "Design of Circularly Polarized Triple-Band Wearable Textile Antenna with Safe Low SAR for Human Health." Electronics 9, no. 9: 1366.

Chapter
Published: 22 July 2020 in Econometrics for Financial Applications
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Internet of things (IoT), Big Data, and artificial intelligence (AI) are related research fields that have a relevant impact factor on the design and development of enhanced personalized healthcare systems. This paper discussed the review of AI for IoT and medical systems, which include the usage and practice of AI methodology in different fields of healthcare. The literature review shows that four main areas use AI methodology in medicine, such as heart disease diagnosis, predictive methods, robotic surgery, and personalized treatment. The results confirm that k-nearest neighbors, support vector machine, support vector regression, Naive Bayes, linear regression, regression tree, classification tree, and random forest are the leading AI methods. These methods are mainly used for patient’s data analysis to improve health conditions. Robotic surgery systems such as Transoral Robotic Surgery and Automated Endoscopic System for Optimal Positioning lead to several advantages as these methods provide less aggressive treatments and provide better results in terms of blood loss and faster recovery. Furthermore, Internet of medical things addresses numerous health conditions such a vital biophysical parameters supervision, diabetes, and medical decision-making support methods.

ACS Style

Salome Oniani; Gonçalo Marques; Sophio Barnovi; Ivan Miguel Pires; Akash Kumar Bhoi. Artificial Intelligence for Internet of Things and Enhanced Medical Systems. Econometrics for Financial Applications 2020, 43 -59.

AMA Style

Salome Oniani, Gonçalo Marques, Sophio Barnovi, Ivan Miguel Pires, Akash Kumar Bhoi. Artificial Intelligence for Internet of Things and Enhanced Medical Systems. Econometrics for Financial Applications. 2020; ():43-59.

Chicago/Turabian Style

Salome Oniani; Gonçalo Marques; Sophio Barnovi; Ivan Miguel Pires; Akash Kumar Bhoi. 2020. "Artificial Intelligence for Internet of Things and Enhanced Medical Systems." Econometrics for Financial Applications , no. : 43-59.

Journal article
Published: 20 July 2020 in Sensors
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Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diabetes is forecasted. EAGA model is further used with Multilayer Perceptron (MLP) to determine the presence or absence of type 2 diabetes in patients based on the symptoms detected. The proposed classification approach was named as Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP). It is also implemented on seven different disease datasets to assess its impact and effectiveness. Performance of the proposed model was validated against some vital performance metrics. The results show a maximum accuracy rate of 97.76% and 1.12 s of execution time. Furthermore, the proposed model presents an F-Score value of 86.8% and a precision of 80.2%. The method is compared with many existing studies and it was observed that the classification accuracy of the proposed Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP) model clearly outperformed all other previous classification models. Its performance was also tested with seven other disease datasets. The mean accuracy, precision, recall and f-score obtained was 94.7%, 91%, 89.8% and 90.4%, respectively. Thus, the proposed model can assist medical experts in accurately determining risk factors of type 2 diabetes and thereby help in accurately classifying the presence of type 2 diabetes in patients. Consequently, it can be used to support healthcare experts in the diagnosis of patients affected by diabetes.

ACS Style

Sushruta Mishra; Hrudaya Kumar Tripathy; Pradeep Kumar Mallick; Akash Kumar Bhoi; Paolo Barsocchi. EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors 2020, 20, 4036 .

AMA Style

Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Akash Kumar Bhoi, Paolo Barsocchi. EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors. 2020; 20 (14):4036.

Chicago/Turabian Style

Sushruta Mishra; Hrudaya Kumar Tripathy; Pradeep Kumar Mallick; Akash Kumar Bhoi; Paolo Barsocchi. 2020. "EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis." Sensors 20, no. 14: 4036.

Journal article
Published: 14 July 2020 in Materials
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This research presents the parametric effect of machining control variables while turning EN31 alloy steel with a Chemical Vapor deposited (CVD) Ti(C,N) + Al2O3 + TiN coated carbide tool insert. Three machining parameters with four levels considered in this research are feed, revolutions per minute (RPM), and depth of cut (ap). The influences of those three factors on material removal rate (MRR), surface roughness (Ra), and cutting force (Fc) were of specific interest in this research. The results showed that turning control variables has a substantial influence on the process responses. Furthermore, the paper demonstrates an adaptive neuro fuzzy inference system (ANFIS) model to predict the process response at various parametric combinations. It was observed that the ANFIS model used for prediction was accurate in predicting the process response at varying parametric combinations. The proposed model presents correlation coefficients of 0.99, 0.98, and 0.964 for MRR, Ra, and Fc, respectively.

ACS Style

Ishwer Shivakoti; Lewlyn L. R. Rodrigues; Robert Cep; Premendra Mani Pradhan; Ashis Sharma; Akash Kumar Bhoi. Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel. Materials 2020, 13, 3137 .

AMA Style

Ishwer Shivakoti, Lewlyn L. R. Rodrigues, Robert Cep, Premendra Mani Pradhan, Ashis Sharma, Akash Kumar Bhoi. Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel. Materials. 2020; 13 (14):3137.

Chicago/Turabian Style

Ishwer Shivakoti; Lewlyn L. R. Rodrigues; Robert Cep; Premendra Mani Pradhan; Ashis Sharma; Akash Kumar Bhoi. 2020. "Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel." Materials 13, no. 14: 3137.

Journal article
Published: 08 July 2020 in Materials
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In this article, an improved variant of the cuckoo search (CS) algorithm named Coevolutionary Host-Parasite (CHP) is used for maximizing the metal removal rate in a turning process. The spindle speed, feed rate and depth of cut are considered as the independent parameters that describe the metal removal rate during the turning operation. A data-driven second-order polynomial regression approach is used for this purpose. The training dataset is designed using an L16 orthogonal array. The CHP algorithm is effective in quickly locating the global optima. Furthermore, CHP is seen to be sufficiently robust in the sense that it is able to identify the optima on independent reruns. The CHP predicted optimal solution presents ±10% deviations in the optimal process parameters, which shows the robustness of the optimal solution.

ACS Style

Kanak Kalita; Ranjan Kumar Ghadai; Lenka Cepova; Ishwer Shivakoti; Akash Kumar Bhoi. Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron. Materials 2020, 13, 3047 .

AMA Style

Kanak Kalita, Ranjan Kumar Ghadai, Lenka Cepova, Ishwer Shivakoti, Akash Kumar Bhoi. Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron. Materials. 2020; 13 (14):3047.

Chicago/Turabian Style

Kanak Kalita; Ranjan Kumar Ghadai; Lenka Cepova; Ishwer Shivakoti; Akash Kumar Bhoi. 2020. "Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron." Materials 13, no. 14: 3047.

Journal article
Published: 04 June 2020 in Information
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Massive multi-input-multi-output (MIMO) systems are the future of the communication system. The proper design of the MIMO system needs an appropriate choice of detection algorithms. At the same time, Lattice reduction (LR)-aided equalizers have been well investigated for MIMO systems. Many studies have been carried out over the Korkine–Zolotareff (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms. This paper presents an analysis of the channel capacity of the massive MIMO system. The mathematical calculations included in this paper correspond to the channel correlation effect on the channel capacity. Besides, the achievable gain over the linear receiver is also highlighted. In this study, all the calculations were further verified through the simulated results. The simulated results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE), integer forcing (IF) receivers with log-likelihood ratio (LLR)-ZF, LLR-MMSE, KZ-ZF, and KZ-MMSE. The main objective of this work is to show that, when a lattice reduction algorithm is combined with the convention linear MIMO receiver, it improves the capacity tremendously. The same is proven here, as the KZ-MMSE receiver outperforms its counterparts in a significant margin.

ACS Style

Samarendra Nath Sur; Rabindranath Bera; Akash Kumar Bhoi; Mahaboob Shaik; Gonçalo Marques. Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems. Information 2020, 11, 301 .

AMA Style

Samarendra Nath Sur, Rabindranath Bera, Akash Kumar Bhoi, Mahaboob Shaik, Gonçalo Marques. Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems. Information. 2020; 11 (6):301.

Chicago/Turabian Style

Samarendra Nath Sur; Rabindranath Bera; Akash Kumar Bhoi; Mahaboob Shaik; Gonçalo Marques. 2020. "Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems." Information 11, no. 6: 301.

Journal article
Published: 30 May 2020 in Micromachines
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A compact textile ultra-wideband (UWB) antenna with an electrical dimension of 0.24λo × 0.24λo × 0.009λo with microstrip line feed at lower edge and a frequency of operation of 2.96 GHz is proposed for UWB application. The analytical investigation using circuit theory concepts and the cavity model of the antenna is presented to validate the design. The main contribution of this paper is to propose a wearable antenna with wide impedance bandwidth of 118.68 % (2.96–11.6 GHz) applicable for UWB range of 3.1 to 10.6 GHz. The results present a maximum gain of 5.47 dBi at 7.3 GHz frequency. Moreover, this antenna exhibits Omni and quasi-Omni radiation patterns at various frequencies (4 GHz, 7 GHz and 10 GHz) for short-distance communication. The cutting notch and slot on the patch, and its effect on the antenna impedance to increase performance through current distribution is also presented. The time-domain characteristic of the proposed antenna is also discussed for the analysis of the pulse distortion phenomena. A constant group delay less than 1 ns is obtained over the entire operating impedance bandwidth (2.96–11.6 GHz) of the textile antenna in both situations, i.e., side by side and front to front. Linear phase consideration is also presented for both situations, as well as configurations of reception and transmission. An assessment of the effects of bending and humidity has been demonstrated by placing the antenna on the human body. The specific absorption rate (SAR) value was tested to show the radiation effect on the human body, and it was found that its impact on the human body SAR value is 1.68 W/kg, which indicates the safer limit to avoid radiation effects. Therefore, the proposed method is promising for telemedicine and mobile health systems.

ACS Style

Ashok Yadav; Vinod Kumar Singh; Akash Kumar Bhoi; Gonçalo Marques; Begonya Garcia-Zapirain; Isabel De La Torre Díez. Wireless Body Area Networks: UWB Wearable Textile Antenna for Telemedicine and Mobile Health Systems. Micromachines 2020, 11, 558 .

AMA Style

Ashok Yadav, Vinod Kumar Singh, Akash Kumar Bhoi, Gonçalo Marques, Begonya Garcia-Zapirain, Isabel De La Torre Díez. Wireless Body Area Networks: UWB Wearable Textile Antenna for Telemedicine and Mobile Health Systems. Micromachines. 2020; 11 (6):558.

Chicago/Turabian Style

Ashok Yadav; Vinod Kumar Singh; Akash Kumar Bhoi; Gonçalo Marques; Begonya Garcia-Zapirain; Isabel De La Torre Díez. 2020. "Wireless Body Area Networks: UWB Wearable Textile Antenna for Telemedicine and Mobile Health Systems." Micromachines 11, no. 6: 558.