International Journal of Computer and Applications

https://link.springer.com/content/pdf/10.1057%2Fjors.1996.159.pdf

Modeling and Analysis of Stochastic Systems

Advancements in the field of autonomous underwater vehicle

Underwater communication remains a challenging technology via communication cables and the cost of underwater sensor network (UWSN) deployment is still very high. As an alternative, underwater wireless communication has been proposed and have received more attention in the last decade. Preliminary research indicated that the Radio Frequency (RF) and Magneto-Inductive (MI) communication achieve higher data rate in the near field communication. The optical communication achieves good performance when limited to the line-of-sight positioning. The acoustic communication allows long transmission range. However, it suffers from transmission losses and time-varying signal distortion due to its dependency on environmental properties. These latter are salinity, temperature, pressure, depth of transceivers, and the environment geometry. This paper is focused on both the acoustic and magneto-inductive communications, which are the most used technologies for underwater networking. Such as acoustic communication is employed for applications requiring long communication range while the MI is used for real-time communication. Moreover, this paper highlights the trade-off between underwater properties, wireless communication technologies, and communication quality. This can help the researcher community by providing clear insight for further research.

Underwater Wireless Sensor Networks: A Survey on Enabling Technologies, Localization Protocols, and Internet of Underwater Things

Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model

In the present research work, we have investigated density of electronic states, electronic band structure, magnetic structure, optical spectra and temperature dependent thermodynamic characteristics of thallium rare earth sulphides, TlRES2 (RE: Tb-Er) using FP-LAPW method and PBE-GGA exchange correlation within DFT. The electronic structure pointed out that TlRES2 compounds show half metallic character. The magnetic moment and spin polarization calculations prove that TlRES2 are fully spin polarized compounds with ferromagnetic nature. Optical spectra show that intraband transitions occur in infrared (IR) regime due to half metallic character of TlRES2 and behave as opaque in ultraviolet (UV) region. High refractive index in IR regime also show metallic character and high peak in UV regime show slow propagation of light in UV regime. The reflection coefficient is found maximum (~50–60%) in UV regime. Greater energy loss has been seen on the higher energy side, which corresponds to the stimulation of plasmons by electrons moving through the compounds. Plasma resonances lead to the frequent peaks at ~ 6.0 eV, 11.0 eV, and 12.0 eV. According to calculations made using the quasi-harmonic Debye model, TlRES2 compounds are dimensionally less stable (i.e., much hard), have poor thermal conductivity, and experience faster-than-average disorder with increasing the temperature.

https://ojs.wiserpub.com/index.php/MP/article/download/2857/1463

Study of Magnetic, Optical, Electronic and Thermodynamic Effects in Thallium Rare Earth Disulphides (TlRES2, RE= Tb- Er)

Theoretical investigations on electronic and optical properties of half heusler alloy,FeNbSb for opto-electronic applications

 Various studies have proved that linear traits have strong relationship with milk productivity but no such models are available for selection of animals based on linear traits. The present study conducted during 2020-22, is an attempt to develop an intelligent model using machine learning algorithms to predict peak milk yield based on its linear traits for selection of best dairy animals. A dataset on 14 linear traits of 259 buffalos across 5 lactations with peak milk yield was created and used for developing models. Data was collected from the buffalos having 8 to 26 kg peak milk yield maintained at Animal Farm Section, Central Institute for Research on Buffaloes, Hisar and also from private farms maintained by farmers. Predictive models were developed using various machine learning algorithms (artificial neural network, support vector regression and random forest) along with multi-linear regression executed on WEKA machine learning platform. Performance of these models was evaluated using evaluation metrics root mean squared error (RMSE). Results revealed that the Artificial Neural Network (ANN) model performed best with minimum RMSE 2.0308. Rear udder height and Lactation number emerged as the two most important attributes affecting the peak milk yield. Such model will be useful and handy for the stakeholders in selection of best dairy animals based on linear traits in absence of authentic record of peak milk yield.

Machine learning algorithms for predicting peak yield in buffaloes using linear traits

3 Abstract: Geographical routing has been widely deployed by many researchers to overcome the pitfalls of traditional routing strategies. Initially, geographical routing is merely based on location service and forwarding strategies. But this paper presents the review of conceptual and application specific idea behind their development. We investigate the ways that defines which type, how and swhy new geographical routing algorithms are developed. This paper have reviewed new paths towards development of other location based routing protocols with advanced feature. This paper represents an overview on the benefits as well as limitations of location based routing protocols on the basis of their evolution like routing with or without location information, network topological challenges, transmission ranges problem and Link Asymmetry solution.

Incremental Development of Geographical Routing Protocols for WSNs: A Review

Selection of high producing dairy animals is important for dairy profitability and future breeding stock. Thefarmers have relied on physical characters for identification of milk producing ability in animals. In the presentstudy feature selection algorithm were implemented to identify most relevant traits for prediction of peak milk yieldin buffaloes. Based on data recorded from 259 lactating Murrah buffaloes, 14 different body and udder conformation traits, viz. Body Length (BL), Height at Wither (HW), Heart Girth (HG), Body Depth (BD), Paunch Girth (PG), Naval-Udder Distance (NUD), Udder Depth (UD), Rear Udder Height (RUH), Fore Teat Distance (FTD), RearTeat Distance (RTD), Fore Rear Teat Distance (FRTD), Teat Length (TL), Rump Width (RW) and Rear UdderWidth (RUW) were selected. Descriptive statistical analysis revealed that the correlation with peak yield is highestfor RUH, followed RUW, lactation number (LN), NUD, FRTD, HG, RW, RTD, UD, TL, PG, BL, BD, HW andFTD. Correlation-based feature selection in ‘WEKA’ software platform suggested that nine parameters have highcorrelation with peak yield – UD, NUD, RTD, FRTD, TL, RW, RUW, RUH and TL. The Multiple linear regression(MLR) was implemented using the linear regression function available under function classifier in WEKA. TwoRegression models (Model 1 and Model 2) were developed using all fifteen input parameters and with subset of 9input parameters suggested in ‘feature selection’. All models were trained and validated with 10-fold cross validation method. The performance of models developed for prediction peak milk yield was evaluated using the metrics correlation coefficient and root mean squared error (RMSE). Comparison of the performance evaluation matrices revealed that the Model 2 requiring lesser number of inputs is good enough in predicting peak yield with 0.8429 correlation coefficient and 2.16 root mean squared error.

Rishi Pal Singh

Papers

Study of Magnetic, Optical, Electronic and Thermodynamic Effects in Thallium Rare Earth Disulphides (TlRES2, RE= Tb- Er)

Theoretical investigations on electronic and optical properties of half heusler alloy,FeNbSb for opto-electronic applications

Machine learning algorithms for predicting peak yield in buffaloes using linear traits

Incremental Development of Geographical Routing Protocols for WSNs: A Review

An algorithm-based approach for identification of most relevant linear traits for selecting high producing Murrah buffaloes