Computer Science Research Papers/Topics

Unmanned aerial vehicle object recognition in bad weather using dark channel prior and convolutional neural networks

Abstract: Object recognition using Unmanned Aerial Vehicles (UAVs) is increasingly becoming more useful. Tremendous success has been achieved on UAV object recognition in clear weather conditions where adequate illumination makes it easier for UAVs to recognize objects in the scene. Unfortunately, for outdoor applications, there is no escape from bad weather moments such as haze, fog, dust, smoke and smog. These weather nuisances occur due to suspended particles in the atmosphere, ultimately...

BA-LEACHC: biphase authentication based LEACH-C protocol for wireless sensor networks

Abstract: The intention in Wireless Sensor Networks (WSNs) is to group sensed raw facts from geographical area under monitoring. Yet, sensor nodes sometimes report tempered (falsified) information resulting from malicious nodes. Hence, various studies recommend pre-identification of malicious node as essential for reliable and healthy networks. Different techniques have been proposed to deal with bogus sensor nodes in WSNs, ranging from authentication to trust based approaches. In this ...

Efficient Active Learning Constrains for Improved Semi-Supervised Clustering Performance

Abstract: This paper presents a semi supervised clustering technique with incremental and decremented affinity propagation (ID-AP) that structures labeled exemplars into the AP algorithm and a new method for actively selecting informative constraints to make available of improved clustering performance. The clustering and active learning methods are both scalable to large data sets, and can hold very high dimensional data. In this paper, the active learning challenges are examined to choose ...

A predictive typological content retrieval method for real-time application using multilingual natural language processing

Abstract: Natural language processing (NLP) is widely used in multi-media real-time applications for understanding human interactions through computer aided-analysis. NLP is common in auto-filling, voice recognition, typo-checking applications, and so forth. Multilingual NLP requires vast data processing and interaction recognition features for leveraging content retrieval precision. To strengthen this concept, a predictive typological content retrieval method is introduced in this article. ...

Feature Selection Methods: a Survey for Effective Data Classification

Abstract: Data is growing every day. This daily growing data is called Big Data. The capturing and accessing and preserving of these data is different and difficult. If capturing and preserving of data became very difficult, then classifying data according to the need is also a difficult process. These modern days datasets are high dimensional that is with hundreds of features and keeps on increasing in future. But these hundreds of data are not required for an effective data classification....

Similarity-Based Gene Duplication Prediction in Protein-Protein Interaction Using Deep Artificial Ecosystem Network

Abstract: In the living organism, almost entire cell functions are performed by protein-protein interactions. As experimental and computing technology advances, yet more Protein-Protein Interaction (PPI) data becomes processed, and PPI networks become denser. The traditional methods utilize the network structure to examine the protein structure. Still, it consumes more time and cost and creates computing complexity when the system has gene duplications and a complementary interface. This res...

A review of deep learning models to detect malware in Android applications

Abstract: Android applications are indispensable resources that facilitate communication, health monitoring, planning, data sharing and synchronization, social interaction, business and financial transactions. However, the rapid increase in the smartphone penetration rate has consequently led to an increase in cyberattacks. Smartphone applications use permissions to allow users to utilize different functionalities, making them susceptible to malicious software (malware). Despite the rise in ...

A Novel Approach for Analysis and Prediction of Students Academic Performance Using Machine Learning Algorithms

Abstract: Educational data mining has become an efective tool for exploring the hidden relationships in educational data and predicting students’ academic performance. The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. To achieve qualitative education standard, several attempts have been m...

Future of Internet of Everything (IOE).

Abstract: In the world, the man matters, not the machine, people need to be care, not data. There is transition from information technology to human technology. The answer is internet of everything (IoE). The int ernet of everything (IoE) is a concept that extends the internet of things (IOT) by encompassing the machine-to-machine (M2M) communication, machine-to-people (M2P) and technology-assisted people-to-people (P2P) with expended digital features. It includes different types of devices,...

A survey on soft computing-based high-utility item sets mining

Abstract: Traditional frequent itemsets mining (FIM) suffers from the vast memory cost, small processing speed and insufficient disk space requirements. FIM assumes only binary frequency value for items in the dataset and assumes equal importance value for items. In order to target all these limitations of FIM, high-utility itemsets (HUIs) mining has been presented. HUIs mining is more complicated and difficult than FIM. HUIs mining algorithms spend more execution time because of large searc...

Epvmp: Enhanced Probabilistic Based Vehicular Multi-Hop Strategies Protocol For Crisis In Vanets Broadcasting

Abstract: Vehicle-to-Vehicle (V2V) transmission is utilized for most security-related programs targeting Vehicular Ad-hoc Networks (VANETs) to disseminate safety-related knowledge to nearby cars. However, the traditional broadcasting networks have broadcast winds, contributing to intolerable pauses in transmission and loss of packets. This paper proposes an important transmission scheme for distributing protection signals throughout the sensitive region called the Enhanced Probabilistic-b...

A context-aware lemmatization model for setswana language using machine learning

Abstract: Lemmatization is an important task which is concerned with making computers understand the relationship that exists amongst words written in natural language. It is a prior condition needed for the development of natural language processing (NLP) systems such as machine translation and information retrieval. In particular, Lemmatization is intended to reduce the variability in word forms by collapsing related words to a standard lemma. There is a limited research on lemmatization o...

Multi-source reliable multicast routing with QoS constraints of NFV in edge computing

Abstract: Edge Computing (EC) allows processing to take place near the user, hence ensuring scalability and low latency. Network Function Virtualization (NFV) provides the significant convenience of network layout and reduces the service operation cost in EC and data center. Nowadays, the interests of the NFV layout focus on one-to-one communication, which is costly when applied to multicast or group services directly. Furthermore, many artificial intelligence applications and services of cl...

Algorithm for compressing/decompressing Sudoku grids

Abstract: We describe a way to transfer efficiently Sudoku grids through the Internet. This is done by using linearization together with compression and decompression that use the information structure present in all sudoku grids. The compression and the corresponding decompression are based on the fact that in each Sudoku grid there are information dependencies and so some of the information is redundant.

Delay bounded multi-source multicast in software-defined networking

Abstract: Software-Defined Networking (SDN) is the next generation network architecture with exciting application prospects. The control function in SDN is decoupled from the data forwarding plane, hence it provides a new centralized architecture with flexible network resource management. Although SDN is attracting much attention from both industry and research, its advantage over the traditional networks has not been fully utilized. Multicast is designed to deliver content to multiple desti...


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