With the advancements in data mining, wearables, and cloud computing, online disease diagnosis se... more With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset ...
2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)
The big data are data in large size and which cannot be maintained in terms of bytes. Know a day&... more The big data are data in large size and which cannot be maintained in terms of bytes. Know a day's big data is so popular that almost every business is depends on data. The data can arise in various forms such as structured, unstructured and semi structured. The different sources of big data generations are through social media such as face book, Google forums, and search engines. These data will be used by different enterprises to make their decision making. Hence big data analysis is a big challenge because data is time variant. This paper presents different techniques used to process big data and various challenges faced by analysts while making decision making. This paper also gives description of various frameworks available to process a big data. The scope of this paper is to find best possible technique to process big data.
2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)
In this digital age, natural language is causing hindrance in the advancement of information tech... more In this digital age, natural language is causing hindrance in the advancement of information technology revolution in India. There is a need to perform Natural Language Processing (NLP) using computer processing, so that computer based systems can be accessible through natural languages like Hindi. Therefore a language translator is very important tool to resolve this problem. One of the key challenges involved in the design of a language translator is Polysemy disambiguation. Polysemy means having two or more meanings to a single word. For language translation operations, it is crucial to find the right meaning of any given word in its context. This is known as Word Sense Disambiguation (WSD). Various resources and tools already exist for WSD in English language while regional languages have not been equally explored yet. This project has a user interface where the user enters a meaningful sentence in Hindi with polysemy word. The system identifies the polysemous word/s if any in the sentence and lists one or more meanings associated with the polysemous word. Using existing machine learning techniques the project identifies the right meaning of the polysemous word based on the given context.
Wireless networks include a set of nodes which are connected to one another via wireless links fo... more Wireless networks include a set of nodes which are connected to one another via wireless links for communication purposes. Wireless sensor networks (WSN) are a type of wireless network, which utilizes sensor nodes to collect and communicate data. Node localization is a challenging problem in WSN which intends to determine the geographical coordinates of the sensors in WSN. It can be considered an optimization problem and can be addressed via metaheuristic algorithms. This study introduces an elite oppositional farmland fertility optimization-based node localization method for radio communication networks, called EOFFO-NLWN technique. It is the goal of the proposed EOFFO-NLWN technique to locate unknown nodes in the network by using anchor nodes as a starting point. As a result of merging the principles of elite oppositional-based learning (EOBL) and the agricultural fertility optimization algorithm (FFO), we have developed the EOFFO-NLWN approach, which is described in detail below....
Concurrency and Computation: Practice and Experience
SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is... more SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT‐based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy‐efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research descr...
With the advancements in data mining, wearables, and cloud computing, online disease diagnosis se... more With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset ...
2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)
The big data are data in large size and which cannot be maintained in terms of bytes. Know a day&... more The big data are data in large size and which cannot be maintained in terms of bytes. Know a day's big data is so popular that almost every business is depends on data. The data can arise in various forms such as structured, unstructured and semi structured. The different sources of big data generations are through social media such as face book, Google forums, and search engines. These data will be used by different enterprises to make their decision making. Hence big data analysis is a big challenge because data is time variant. This paper presents different techniques used to process big data and various challenges faced by analysts while making decision making. This paper also gives description of various frameworks available to process a big data. The scope of this paper is to find best possible technique to process big data.
2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)
In this digital age, natural language is causing hindrance in the advancement of information tech... more In this digital age, natural language is causing hindrance in the advancement of information technology revolution in India. There is a need to perform Natural Language Processing (NLP) using computer processing, so that computer based systems can be accessible through natural languages like Hindi. Therefore a language translator is very important tool to resolve this problem. One of the key challenges involved in the design of a language translator is Polysemy disambiguation. Polysemy means having two or more meanings to a single word. For language translation operations, it is crucial to find the right meaning of any given word in its context. This is known as Word Sense Disambiguation (WSD). Various resources and tools already exist for WSD in English language while regional languages have not been equally explored yet. This project has a user interface where the user enters a meaningful sentence in Hindi with polysemy word. The system identifies the polysemous word/s if any in the sentence and lists one or more meanings associated with the polysemous word. Using existing machine learning techniques the project identifies the right meaning of the polysemous word based on the given context.
Wireless networks include a set of nodes which are connected to one another via wireless links fo... more Wireless networks include a set of nodes which are connected to one another via wireless links for communication purposes. Wireless sensor networks (WSN) are a type of wireless network, which utilizes sensor nodes to collect and communicate data. Node localization is a challenging problem in WSN which intends to determine the geographical coordinates of the sensors in WSN. It can be considered an optimization problem and can be addressed via metaheuristic algorithms. This study introduces an elite oppositional farmland fertility optimization-based node localization method for radio communication networks, called EOFFO-NLWN technique. It is the goal of the proposed EOFFO-NLWN technique to locate unknown nodes in the network by using anchor nodes as a starting point. As a result of merging the principles of elite oppositional-based learning (EOBL) and the agricultural fertility optimization algorithm (FFO), we have developed the EOFFO-NLWN approach, which is described in detail below....
Concurrency and Computation: Practice and Experience
SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is... more SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT‐based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy‐efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research descr...
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Papers by JAGADISH .S. KALLIMANI