European Journal of Molecular & Clinical Medicine

The in-silico method is extensively utilized in the study of proteomics and genomics studies.
The T-cell epitopes prediction is essential step in the development of peptide -based
vaccines and diagnostic. The epitopes emanate as an emanation of intricate proteolytic
mechanism within cell. Proceed to being perceived by T cells, an epitope is presented on
the cell surface as a complex with a major histocompatibility complex protein. Henceforth,
T-Cell identified epitopes are excellent binder of MHC. Therefore detection and
identification of the MHC binders essential for target based study of drug. In recent study,
we analyzed D. medinensis antigenic protein peptide binders to MHC-I and MHC-II
molecules. The binding with MHC-I molecules are obtained with are 11mer_H2_Db,
10mer_H2_Db, 9mer_H2_Db, 8mer_H2_Db and for MHC-II are as I_Ab.p, I_Ad.p,
I_Ag7.p, I_Ak.p .

This paper presents a study on minimising the estimation error of forecasting the
electricity consumption in Malaysia. A robust and accurate forecasts of electricity
consumption are deemed crucial for the supplier to arrive on fair estimations of electricity
supply optimally. Thus, identifying the best model to forecast the electricity consumption
accurately may hinder energy wastage. This research aims to examine which model gives
the least error in estimating the future electricity consumptions in Malaysia. Two models
were tested namely Artificial Neural Network (ANN) and Regression Methods. In analysing
these models, this research applies the Microsoft Excel and SAS Enterprise Miner (SAS)
software. The data were extracted from the Department of Statistics Malaysia (DOSM),
CEIC Data Company and The Statistics Portal. Results indicate that ANN produces least
error as compared to the Regression Method as the former fits the data well whilst the latter
overfits the data. The ANN model uses NNTool from MATLAB is used for forecasting
future electricity consumption. The forecasted values (2020-2022) proved to provide more
interpretable forecasts. This study may benefit the electricity supplier, consumers and also
the Government of Malaysia, in particular the Ministry of Energy and Natural Resources.
It may provide insights on estimating the optimum amount of energy to be generated. This
will definitely increase the savings and reduce wastage from every angle. Ultimately, the
environment is saved too.

Healthcare is a predictable task to wipe out human life. Coronary heart disease is sickness that impacts the human coronary heart. Cardiovascular sicknesses will forecast with the aid of several techniques that helped in making choices about the modifications that maintain excessive-risk patients which resulted in the discount of their dangers. The purpose of demise ratio of those sicknesses may be very high. It is very imperative to become aware of if the individual has heart disorder or now not. In medical field it is very important to find the occurrence of prediction of the heart diseases. Accurate Prediction results are very efficient to treat the patient’s medical history before the attack occurs. The techniques Data mining and Machine learning plays a essential role to predict the occurrence of heart diseases. These techniques diagnose these diseases with the help of dataset in healthcare centers. Various models used to reduce the number of deaths ratio. Models based on several algorithms such as Support Vector Machine (SVM), Decision Tree(DT), Naïve Bayes(NB), K-Nearest Neighbor(KNN), and Artificial Neural Network (ANN) are implemented to predict heart disease. The accuracy of these models helps to diagnose the diseases with better results. This paper summarized the performance of all algorithms which are used to predict and diagnose heart diseases.

With the increasing size of supply and demand growth in the next decades there will be
need of smarter distribution system. The installation of large number of electric power
generation units may have adverse impact on environment. So smart energy management
system is one of the best and novel approaches which enables smart grid operations.
During the installation and incorporation of smart grids there are some factors like
consumption of electric energy, energy storage, and generation resources should be
optimized in such a manner that saves energy, improves efficiency, maintains security and
enhances reliability during increasing demand at minimum operating cost. Some of the
renewable energy sources may be taken as the pillars for making smart energy buildings
which reduces the cost of building systems. From the point of distributed generation it may
be considered as future power generation by the installation of renewable energy systems
and storage systems. It will lead into smart energy buildings which will be in the form of
Off-grid/Hybrid/Grid tied based solar system. Due to the development of smart techniques
like fuzzy systems and artificial neural network system it is helpful to reduce billing cost of
energy building systems. Green house gas emission is also a serious concern during the
installation of energy buildings so hydro or wind energy systems are fully weather
dependent and they can reach up to only 14% generation of electricity due to intermittent
sources in nature. To overcome the problem of more energy demand and gas emission a
new method proposed such as smart system services for the improvement of building
performance. This paper deals with advanced techniques for smart home energy
management system in order to control its operations in reliable, secure and economical
manner.

Segmentation of Pancreas with high accuracy in computerized tomography (CT) results is considered to be a basic issue in both medical image processing and computer-aided diagnosis (CAD). Pancreas segmentation is considered as a difficult task due to its uncertainity in location and in analysis of organs, while it takes very minute division of the entire abdominal CT scans. Because of the accelerated development of the CAD system and therefore the serious need for antiseptic treatments, pancreas segmentation with high accuracy of results is demanded. A new approach is used in this paper, for automated pancreas segmentation of CT images using inter-/intra-slice circumstancial instruction with preprocessing, segmentation, feature extraction, classification.

The speech signal moving objects regarding the speaker’s personality. A speaker recognition field is about retrieving the name of the individual voicing the speech. The effectiveness of accurately identifying a speaker is focused solely on vocal features, as voice contact with machines is becoming more prevalent in tasks like telephone, banking transactions, and the transformation of data from speech databases. This review illustrates the detection of text-dependent speakers, which identifies a single speaker from a known population. The program asks the user to utter voice. Program recognizes the person through evaluating the voice utterance codebook with the voice utterance codebook held in the database and records that may have provided the voice speech. Furthermore, the features are removed; the speech signal is registered for 6 speakers. Extraction of the function is achieved using LPC coefficients, AMDF calculation and DFT. By adding certain features as input data, the neural network is equipped. For further comparison the characteristics are stored in models. The characteristics that need to be defined for the speakers were obtained and analyzed using Back Propagation Algorithm to a template image. Now this framework trained correlates to the outcome; the source is the characteristics retrieved from the speaker to be described. The weight adjustment is done by the system, and the similarity score is discovered to recognize the speaker. The number of iterations needed for achieving the goal determines the efficiency of the network.