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Arudra Annepu ◽  
Priti Mishra ◽  

Wireless network technically, refers to the category of network in which communication is carried out without using wires. In modern era wireless network has great importance because the communication is taking place with the use of radio waves. Thus, the use of ad-hoc network starts yielding a great importance in variety of applications. The certain research work is carried out in this particular field. MANET is a constructed from various mobility in the form of mobile nodes and anytime without any need of fixed infrastructure. MANET can be made on fly due to lack of fixed infrastructure. MANET is numerous threats types of attacks due to dynamic changing topologies and wireless medium. Security of the MANET becomes one of the challenging tasks. Black hole attacks is the main type of attack that are possible in MANET. Black hole node not forward any data packets to the neighbour node instead it drops all the data packets. Black hole attacks are bit hard to detect due to lack of centralized access. This research work concentrates to enhance the security of MANET by identifying and blocking black hole assaults from occurring. A reactive routing system such as Ad-Hoc on Demand Distance Vector has previously been used to address security problems in the MANET (AODV). Various attack types were investigated, and the consequences of these assaults were detailed by describing how MANET performance was disrupted. Network Simulator 3 (NS3) is used for the simulation process.

2022 ◽  
Vol 54 (7) ◽  
pp. 1-39
Christian Berger ◽  
Philipp Eichhammer ◽  
Hans P. Reiser ◽  
Jörg Domaschka ◽  
Franz J. Hauck ◽  

Internet-of-Things (IoT) ecosystems tend to grow both in scale and complexity, as they consist of a variety of heterogeneous devices that span over multiple architectural IoT layers (e.g., cloud, edge, sensors). Further, IoT systems increasingly demand the resilient operability of services, as they become part of critical infrastructures. This leads to a broad variety of research works that aim to increase the resilience of these systems. In this article, we create a systematization of knowledge about existing scientific efforts of making IoT systems resilient. In particular, we first discuss the taxonomy and classification of resilience and resilience mechanisms and subsequently survey state-of-the-art resilience mechanisms that have been proposed by research work and are applicable to IoT. As part of the survey, we also discuss questions that focus on the practical aspects of resilience, e.g., which constraints resilience mechanisms impose on developers when designing resilient systems by incorporating a specific mechanism into IoT systems.

2022 ◽  
Vol 54 (7) ◽  
pp. 1-35
Uttam Chauhan ◽  
Apurva Shah

We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word embedded topic models, and topic models in multilingual perspectives. Besides, the research work for topic modeling in a distributed environment, topic visualization approaches also have been explored. We also covered the implementation and evaluation techniques for topic models in brief. Comparison matrices have been shown over the experimental results of the various categories of topic modeling. Diverse technical challenges and future directions have been discussed.

Sunita Warjri ◽  
Partha Pakray ◽  
Saralin A. Lyngdoh ◽  
Arnab Kumar Maji

Part-of-speech (POS) tagging is one of the research challenging fields in natural language processing (NLP). It requires good knowledge of a particular language with large amounts of data or corpora for feature engineering, which can lead to achieving a good performance of the tagger. Our main contribution in this research work is the designed Khasi POS corpus. Till date, there has been no form of any kind of Khasi corpus developed or formally developed. In the present designed Khasi POS corpus, each word is tagged manually using the designed tagset. Methods of deep learning have been used to experiment with our designed Khasi POS corpus. The POS tagger based on BiLSTM, combinations of BiLSTM with CRF, and character-based embedding with BiLSTM are presented. The main challenges of understanding and handling Natural Language toward Computational linguistics to encounter are anticipated. In the presently designed corpus, we have tried to solve the problems of ambiguities of words concerning their context usage, and also the orthography problems that arise in the designed POS corpus. The designed Khasi corpus size is around 96,100 tokens and consists of 6,616 distinct words. Initially, while running the first few sets of data of around 41,000 tokens in our experiment the taggers are found to yield considerably accurate results. When the Khasi corpus size has been increased to 96,100 tokens, we see an increase in accuracy rate and the analyses are more pertinent. As results, accuracy of 96.81% is achieved for the BiLSTM method, 96.98% for BiLSTM with CRF technique, and 95.86% for character-based with LSTM. Concerning substantial research from the NLP perspectives for Khasi, we also present some of the recently existing POS taggers and other NLP works on the Khasi language for comparative purposes.

Md. Saddam Hossain Mukta ◽  
Md. Adnanul Islam ◽  
Faisal Ahamed Khan ◽  
Afjal Hossain ◽  
Shuvanon Razik ◽  

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

Naveen Lingaraju ◽  
Hosaagrahara Savalegowda Mohan

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

Dr. S. K. Saravanan

Abstract: A chain of blocks that contains information is the definition of Blockchain. The technique is intended to timestamp digital documents so that it is not possible to temper them. The purpose of blockchain is to solve the double records problem without the need of a central server. Blockchain provides a creative approach to storing information, executing transactions, conducting tasks and trust building. Blockchain is an emerging technology for the applications Smart Cities, Smart Grids, Healthcare, Education, Crypto-currency and Supply chain. This research work would offer a detailed analysis of Blockchain in the Educational domain. It also studies the various applications of Blockchain technology. Keywords: Blockchain, Smart Cities, Healthcare, Education, Supply Chain, Privacy, Security.

Rajat Subhra Bhowmick ◽  
Isha Ganguli ◽  
Jayanta Paul ◽  
Jaya Sil

In today’s era of digitization, social media platforms play a significant role in networking and influencing the perception of the general population. Social network sites have recently been used to carry out harmful attacks against individuals, including political and theological figures, intellectuals, sports and movie stars, and other prominent dignitaries, which may or may not be intentional. However, the exchange of such information across the general population inevitably contributes to social-economic, socio-political turmoil, and even physical violence in society. By classifying the derogatory content of a social media post, this research work helps to eradicate and discourage the upsetting propagation of such hate campaigns. Social networking posts today often include the picture of Memes along with textual remarks and comments, which throw new challenges and opportunities to the research community while identifying the attacks. This article proposes a multimodal deep learning framework by utilizing ensembles of computer vision and natural language processing techniques to train an encapsulated transformer network for handling the classification problem. The proposed framework utilizes the fine-tuned state-of-the-art deep learning-based models (e.g., BERT, Electra) for multilingual text analysis along with face recognition and the optical character recognition model for Meme picture comprehension. For the study, a new Facebook meme-post dataset is created with recorded baseline results. The subject of the created dataset and context of the work is more geared toward multilingual Indian society. The findings demonstrate the efficacy of the proposed method in the identification of social media meme posts featuring derogatory content about a famous/recognized individual.

2022 ◽  
Vol 12 ◽  
Shreedhar S. Otari ◽  
Suraj B. Patel ◽  
Manoj M. Lekhak ◽  
Savaliram G. Ghane

Barleria terminalis Nees and Calacanthus grandiflorus (Dalzell) Radlk. are endemic medicinal plants of the Western Ghats of India. The aim of the present research work was to investigate phytochemical profile, potent bioactives using RP-HPLC, LC-MS and GC-MS and to evaluate their bioactivities. Acetone was found to be the best extraction medium for separating phytochemicals. Similarly, acetone and methanol extracts exhibited potential antioxidant properties. Ethanol extract of B. terminalis stem showed potent acetylcholinesterase (AChE) (89.10 ± 0.26%) inhibitory activity. Inhibition of α-amylase (36.96 ± 2.96%) activity was observed the best in ethanol extract of B. terminalis leaves and α-glucosidase inhibitory activity (94.33 ± 0.73%) in ethanol extract of C. grandiflorus stem. RP-HPLC analysis confirmed the presence of several phenolic compounds (gallic acid, hydroxybenzoic acid, vanillic acid, chlorogenic acid and coumaric acid) and phenylethanoid glycoside (verbascoside). The highest phenolics content were observed in B. terminalis (GA (4.17 ± 0.002), HBA (3.88 ± 0.001), VA (4.54 ± 0.001), CHLA (0.55 ± 0.004) mg/g DW, respectively). Similarly, LC-MS and GC-MS revealed the presence of phenolics, glycosides, terpenes, steroids, fatty acids, etc. Moreover, positive correlation between studied phytochemicals and antioxidants was observed in principal component analysis. Based on the present investigation, we conclude that B. terminalis and C. grandiflorus can be further explored for their active principles particularly, phenylethanoid glycosides and iridoids and their use in drug industry for pharmaceutical purposes.

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