Computer science and engineering

Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and ab...

By natural process, the world population will in-crease day by day. In rhythm, the tendency of the food will also be rising. So, it is a great challenge to supply food and increase its availability for the huge amount of people of the world, which helps to fulfill their fundamental civil rights. Nowadays, many agriculturedependent countries are fully dependent on the orthodox agriculture process. To develop such types of conditions, people should attract to modern cultivation practices. In the modern era, the Internet of things (IoT) is an optimal solution for preventing such types of challenges. IoT can connect with neoteric agriculture technology. Byusing this technique, farmers can directly connect themselves and control their felds and monitor their feld environmental conditions from anywhere around the world. By the use of unique forms of sensors, models, strategies, and technologies farmers can collect much real-time information and it helps a bearer to increase his productivi...

Traditional farming is labour-intensive, and the need to constantly check crops may be a strain on farmers. On another side, agricultural yield space is shrinking by the day. So, without a question, managing a huge amount of food that is legally right for us is a very difficult problem for any human being. There may always be a footprint available to meet the high demand. By achieving the idea of smart farming based on new technology by using the Internet of Things (IoT), the authors have presented a strategy in this study work by which a farmer may manage water irrigation, detect the total amount of brightness, and monitor the moisture level of soil and current status of crops using IoT. By utilizing such a technology, the farmer would obtain an auto lighting system, an auto water irrigation system, prohibit external vehicles, and conserve electricity by utilizing real-time data obtained from various types of sensors and utilizing a Wi-Fi system. The suggested system's hardware is all directly linked to the NodeMCU ESP8266. An algorithm has been created to manage the entire project. The solar panel will supply the entire system's necessary electric power, allowing us to save money, conserve electricity, and make the total system more environmentally friendly. This work's suggested system can identify meteorological conditions that are beneficial to agriculture. This proposed concept has exceptional performance potential as an interface between sensors as input and the IoT as an output medium. The suggested system is compared to other existing systems in a variety of ways.

A movie recommendation is important in our social life due to its strength in providing enhanced entertainment. Such a system can suggest a set of movies to users based on their interest, or the popularities of the movies. Although, a set of movie recommendation systems have been proposed, most of these either cannot recommend a movie to the existing users efficiently or to a new user by any means In this paper we propose a movie recommendation system that has the ability to recommend movies to a new user as well as the others. It mines movie databases to collect all the important information, such as, popularity and attractiveness, required for recommendation. It generates movie swarms not only convenient for movie producer to plan a new movie but also useful for movie recommendation. Experimental studies on the real data reveal the efficiency and effectiveness of the proposed system.

Huge amount of movies are available over the world,
all of those are impossible to see for one user and all of them
are not interesting. Movie recommendation systems filter out
irrelevant movies and suggest the relevant movies those would be
interesting for users. Traditional system can not recommend new
users and new items efficiently. In collaborative filtering recommendation is based on users activities and products features hence
when new users enter the system and new items added, it can
not recommend. In content based recommend can recommend
new items based on items features but unable to recommend
new users. Therefor, We have proposed an information mining
tool that collect all important information which is needed in
movie recommendation system. In our proposed system, we have
generated movie swarm which is very useful for movie producers
and can solve new items problem. Also finds out which genres
of movie should be recommended among followers, that solves
new users recommendation problem . Experimental studies on
the real data demonstrate the encourages and effectiveness of
our methods.

In  dynamic  networks,  interactions  that  occur  periodically  express  especially  significant  meaning.
However,  these  patterns  occur  infrequently,  so  it  is  difficult  to  detect  among  mass  data.  To  identify
such periodic patterns in dynamic networks, we propose single pass supergraph based periodic pattern
mining  SPBMiner  technique  that  is  polynomial  unlike  most  graph  mining  problems.  The  proposed
technique  stores all entities in dynamic networks only once and calculates  common sub-patterns only
one time at each timestep.  The performance study shows that SPBMiner method is  time and memory
efficient compare than others. The memory efficiency of our approach is total timesteps independent.

Personalized entertainment items
recommendation is required to help millions of people
narrow the universe of potential items to fit their unique
tastes. These services usually depend on a machine-learning
algorithm, which breaks down items into long lists of
attributes and matches these elements to a user’s
preferences. A set of such algorithms have been proposed.
Most of these, such as collaborative filtering, works by
finding a group of users based on the item he/she buys or
provides feedback and then recommend popular items in
the group. In this paper we argue that relying only on such
acts are not sufficient for an effective recommendation
system, we need to consider other things, such as, an
entertainment item enjoying time since it could change over
the course time. In this paper we proposed a novel
algorithm to find a group of user that usage not only the
ratings but also the time of the given ratings. Additionally,
we propose algorithms for recommending items to the
producers such that they can entertain us more. We
perform experiments to validate the performance of our
system. We show that our system outperforms in
comparison with the existing algorithms.

DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction–modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method “word2vec” and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find...

Software-defined network (SDN) is a new network architecture that is more manageable, adaptable, dynamic and programmable. Though the control plane is separated from the forwarding plane and it is directly programmable these make the network to become fast and easy to manage. SDN technology increases network capability and shortens the network configuration and resource management for its programmable features. For forwarding functions and centralized control, SDN supports Open-Flow technology. There are two forms of routing protocols used in Vehicular ad hoc networks (VANET) such as Proactive routing protocol and Reactive routing protocol. The Reactive routing protocol shows the best performance than others when two communicating devices are linked with each other. In this work, Ad hoc On Demand Distance Vector (AODV) is implemented as a reactive routing protocol in SDN and analyzes the performance based on using the most essential quality of service (QoS) metrics. Moreover, it is found that for VANET in SDN architecture, the highest packet delay is inferior to other traditional network architectures and the packet transfer rate is also high.

We demonstrate, numerically, the generation of supercontinuum using a high-power ultrafast source at 1.06-μm wavelength and a nonlinear hollow-core fiber. We yield a system that is less sensitive to noise by pumping in the normal-dispersion regime, yet still achieves enhanced spectral broadening by exciting an optical soliton in the midinfrared region. The pump pulse goes through a series of nonlinear effects leading to a supercontinuum output that spans across the midinfrared, 0.5-4.2 μm.

We numerically investigate the effect of mode-area dispersion in a tubular-type anti-resonant hollow-core fiber by using a modified generalized nonlinear Schrödinger equation that takes into account the wavelength-dependent mode area in its nonlinear term. The pulse evolution dynamics with and without the effect of mode-area dispersion are compared and analyzed. We show that strong dispersion of the mode area in the proximity of the cladding wall thickness-induced resonances has a significant impact on the soliton pulse propagation, resulting in considerable changes in the conversion efficiencies in nonlinear frequency mixing processes. The differences become more prominent when the pump has higher energy and is nearer to a resonance. Hence, the mode-area dispersion must be accounted for when modeling such a case.

An octagonal photonic crystal fiber (PCF) with an elliptical shape in the center core is numerically investigated for residual dispersion compensation in the wavelength range 1460-1675 nm. The designed fiber exhibits flattened negative dispersion over the S + C + L + U wavelength bands and an average dispersion of −465.5 ps∕nm • km with an absolute dispersion variation of 10.5 ps∕nm • km. In addition, the proposed PCF shows a high birefringence of 2.68 × 10 −2 at the operating wavelength 1550 nm, which meets the requirement of high birefringence. Moreover, the variation of two air holes in the first ring up to 5% ensures an average dispersion of −491.5 ps∕nm • km with a dispersion variation of 13 ps∕nm • km, and birefringence reaches up to 3 × 10 −2. Furthermore, to evaluate the sensitivity of the fiber dispersion properties, 5% variation in the optimum parameters is studied.

Image Caption Generator is a revolutionary technology that combines computer vision with natural language processing to generate descriptive captions for images. This groundbreaking tool has numerous applications in various industries, including social media, e-commerce, and healthcare.