bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weather Prediction using Advanced Machine Learning Techniques
Authors: Hemalatha G. , Srinivasa Rao K. , Arun Kumar D. 
Published: December 2021

Abstract
Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..
Curvelet Transform based Denoising of Multispectral Remote Sensing Images
Authors: Lokeshwara Reddy P. , Pawar Santosh , Prathapa Reddy S.L. 
Published: December 2021

Abstract
With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..
A Real-time Implementation for the Speech Steganography using Short-Time Fourier Transformior Secured Mobile Communication
Authors: Shrivastava Rajeev , Singh Mangal , RakhiThakur  , Teja Kalluri Saidatta Subrahmanya Ravi 
Published: December 2021

Abstract
Steganography can be described as approach of masking an undisclosed message with a normal message which is known as the Carrier message signal. DSP techniques, such as LSB encoding, have historically been implemented for secret information hiding. Utilization ofsteganography functions of deep neural networks for voice data is something this paper will present. This paper also demonstrate that the steganography techniques suggested for vision are less suitable for speech signals this paper present a implementation technique that involves the use of ISTFT and STFT as differentiablelayers in the network. Empirically, the efficacy of the proposed methods based on multiple datasets of speech should be demonstrated and the outcome are examined quantitatively and qualitatively. Using of multiple decoders or a single conditional decoder helps to hide multiple signals in a single carrier signal. Finally, under various channel distortion situations, this model Qualitative studies indicate that human listeners cannot detect changes made to the carrier and hence the decoded messages are highly intelligible.
Design of High Speed and Area Efficient Finite Field Multiplier Using Factoring Technique for Communication
Authors: Baba Fariddin S , Mishra Rahul 
Published: December 2021

Abstract
In this paper, design of high speed and area efficient finite field multiplier using factoring technique for communication is implemented. Data security plays very important role in present generation. Therefore, initially inputs and key are given to S-Box. The main intent of S-Box is to substitute the input data and key. After that input data and key are merged using S-Box merge. This data will be multiplied using finite field multiplier and to improve the performance along with that mix column technique is applied. Factoring technique will increase the speed of operation. After the data performs shift row operation. At last rounding is performed to the obtained data. At last simulation results shows that effective outcome in terms of delay, memory and security.
Artificial Intelligence and Deep Learning for Weapon Identification in Security Systems
Authors: Brahmaiah Makkena , Madala Srinivasa Rao , Mastan Chowdary Ch 
Published: December 2021

Abstract
As crime rates rise at large events and possibly lonely places, security is always a top concern in every field. A wide range of issues may be solved with the use of computer vision, including anomalous detection and monitoring. Intelligence monitoring is becoming more dependent on video surveillance systems that can recognise and analyse scene and anomaly occurrences. Using SSD and Faster RCNN techniques, this paper provides automated gun (or weapon) identification. Use of two different kinds of datasets is included in the proposed approach. As opposed to the first dataset, the second one comprises pictures that have been manually tagged. However, the trade-off between speed and precision in real-world situations determines whether or not each method will be useful.
IMPLEMENTATION OF ELECTRONIC DEVICES OF CMOS FULL ADDER IN LOW POWER VLSI CIRCUITS
Authors: Durgaprasadarao P. , Daya Sagar K.V. 
Published: December 2021

Abstract
Battery-powered devices (for example, mobile phones, digital personal aids, etc) are increasing on the mobile electronic systems market by developing microelectronic circuits with low energy dissipation. The problem of dissipating power could limit the flexibility of the computer system, as the chip’s density and complexity keep on increasing. The power supply consumes approximately 35% of the chip power, particularly at the nanometer level. The purpose of this project is to investigate the efficiency of one of the most reliable low power concepts called Power Gating. It is only nanometer-scale CMOS devices that are the most common technology in existing VLSI systems. Leakage power has become an integral component of total power in the nanometer technology regime. The ALU’s basic feature unit is Full Adder. The electricity consumption of an ALU is decreased by decreasing the energy consumption of an ALU, and an ALU will reduce the power consumption by decreasing the total power consumption. So these days, the complete adder designs are becoming more common with low power characteristics. The proposed project shows the concept of the micro wind tool for low power less transistors.
Medical Analysis for Surgeryon Patients Complaining of Sciatic Pain
Authors: Nakkella Aruna Kumari , Mohan Surapaneni Krishna , Pasupuleti Sri Rama Murthy , Ananda Vayaravel C. 
Published: December 2021

Abstract
A prospective researchwas performed on 50 patients submitted for radiological screening in Samarra general hospital complaining of chronic backache and sciatica. The study was completed for patients recommended by senior neurosurgeons, orthopedicians, &rheumatologist, to the radiological unit in Samarra public hospital from October 2016 to October 2017. Radiological studies were analyzed by senior radiologists of Samarra public hospital in conjunction with the recommending senior doctors. Patients who are operated on were all proven to have chronic Lateral Recess Syndrome both radiological and surgically.
Performance analysis of different classifiers in segmenting point cloud data
Authors: Boslim N I , Abdul Shukor S A , Mohd Isa S N , Wong R 
Published: December 2021

Abstract
3D point clouds are a set of point coordinates that can be obtained by using sensing device such as the Terrestrial Laser Scanner (TLS). Due to its high capability in collecting data and produce a strong density point cloud surrounding it, segmentation is needed to extract information from the massive point cloud containing different types of objects, apart from the object of interest. Bell Tower of Tawau, Sabah has been chosen as the object of interest to study the performance of different types of classifiers in segmenting the point cloud data. A state-of-the-art TLS was used to collect the data. This research’s aim is to segment the point cloud data of the historical building from its scene by using two different types of classifier and to study their performances. Two main classifiers commonly used in segmenting point cloud data of interest like building are tested here, which is Random Forest (RF) and k-Nearest Neighbour (kNN). As a result, it is found out that Random Forest classifier performs better in segmenting the existing point cloud data that represent the historic building compared to k-Nearest Neighbour classifier.
Defects Detection Algorithm of Harumanis Mango for Quality Assessment Using Colour Features Extraction
Authors: Abu Bakar M N , Abdullah A H , Abdul Rahim N , Yazid H , Zakaria N S , Omar S , Wan Nik W M F , Abu Bakar N , Sulaiman S F , Ahmad M I , Ahmad K , Maliki N M , Romle S R 
Published: December 2021

Abstract
Visual defects detection is one of the main problems in the post-harvest processing caused a major production and economic losses in agricultural industry. Manual fruits detection become easy when it is done in small amount, but the result is not consistent which will generate issue in fruit grading. A new fruit quality assessment system is necessary in order to increase the accuracy of classification, more consistencies, efficient and cost effective that would enable the industry to grow accordingly. In this paper, a method based on colour feature extraction for the quality assessment of Harumanis mango is proposed and experimentally validated. This method, including image background removal, defects segmentation and recognition and finally quality classification using Support Vector Machine (SVM) was developed. The results show that the experimental hardware system is practical and feasible, and that the proposed algorithm of defects detection is effective.
Research on effects and influence by presenting information on priority order and oncoming vehicle to operators for teleoperation of multiple autonomous vehicles
Authors: Yokota Mizuki , Tsutsumi Shigeyoshi , Hayakawa Soichiro , Ikeura Ryojun 
Published: December 2021

Abstract
With self-driving vehicles, it is possible to manage multiple vehicles from a remote location even if one observer does not have a driver in the driver’s seat. Therefore, demonstration experiments are being conducted in various places to remotely monitor two autonomous vehicles and operate them as needed. However, when one observer manages multiple vehicles, the amount of information that can be processed is limited. If we can assist with an appropriate amount of information, we may be able to manage more vehicles. In this study, we perform an experiment in which the priority and the type of assist information are changed and presented to the observer in the overtaking scene of a parked vehicle using a simulator. Focusing on the burden on the observer during remote management of multiple units, the purpose is to identify the information required for monitoring and reduce the burden from changes depending on the type of information to be assisted.
Smart Classroom for Electricity-Saving with Integrated IoT System
Authors: Nasrudin Mohd Wafi , Nordin Nur Asyikin , Ismail Iszaidy , Ilman Jais Mohd , Abdul Rahim Amir Nazren , Azani Mustafa Wan 
Published: December 2021

Abstract
Electricity-saving can be achieved through the efficient use of energy such as turning off lights and electrical appliances when not in use. Therefore this work proposed the smart classroom for electricity-saving with an integrated IoT System to prevent wasting electricity in the classroom. Smart Classroom means that it will detect and count the number of students entering and exiting the classroom by using a sensor system automatically. The main objective of this work is to control the lighting systems and fans by using the IoT application and sensor system. This means that when the sensor is triggered the sensor will send data to the Blynk application software using IoT to display the status of the classroom. This proposed work is also able to detect whether a classroom is available to use or not based on the presence of people. If the classroom is being used the Blynk application software will show the lamp and fan are ON. Otherwise the lamps and fans are OFF if there are no people in the classroom. The result successfully shows that if the first student entering the classroom all the lamps and fans are ON. While if the last student exiting the classroom all the lamps and fans are OFF. This result also indicates that electricity can be saved if all appliances in the classroom are switch OFF at the right time.
Development of Solar Powered Sprinkler Robot Based on Watering Plant Using IOT Approach
Authors: You Lim Xin , Shariffudin Nordiana , Zamri Hasan Mohd 
Published: December 2021

Abstract
Nowadays, solar energy’s popularity is growing consistently every year, along with the growth of amazing solar technologies, which is considered to be one of the most popular. Non-renewable energy like petrol and gasoline is being replaced with solar energy, which is renewable energy. The main objective of this project is to design and simulate a robot solar system. The robot is developed using Arduino Mega 2560 as the main brain of the system. This system is equipped with a solar tracking system to track the movement of the sun and LDR is used to detect the presence of sunlight. The solar tracker is used to get the maximum efficiency of solar energy and reduce power losses. In addition, the solar tracker can rotate from 0° - 180°, which is the best angle for the solar panel to reach the sunlight. This robot will be attached to the sprinkler system to perform the watering process. This robot is developed for use in the agriculture field to reduce the manpower and cost of the watering process. Three analyses will be conducted in this project such as solar panel analysis, Wi-Fi connectivity analysis and sprinkler system analysis. The result shows the solar panel will gain the highest intensity of the sunlight at 12.00 pm and a sunny day compared to the other time and a cloudy day. The maximum range of Wi-Fi connectivity and the water pump, time used to finish the watering process and watering area will be discussed.
Comparison of optimized Markov Decision Process using Dynamic Programming and Temporal Differencing – A reinforcement learning approach
Authors: Mani Annapoorni , Bakar Shahriman Abu , Krishnan Pranesh , Yaacob Sazali 
Published: December 2021

Abstract
Reinforcement learning is one of the promising approaches for operations research problems. The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtimes due to non-conforming raw materials. Prediction of the raw material acceptance rate can regulate the raw material supplier selection and improve the manufacturing process by filtering out non-conformities. This paper presents a Markov model developed to estimate the probability of the raw material being accepted or rejected in an incoming inspection environment. The proposed forecasting model is further optimized for efficiency using the two reinforcement learning algorithms (dynamic programming and temporal differencing). The results of the two optimized models are compared, and the findings are discussed.
DEVELOPING AR INTERFACE FOR INTERIOR MODELLING FROM 3D SENSOR
Authors: Sanusi Muhammad Syahmi Bin Ahmad , Shukor S A A , Johari Jalal 
Published: December 2021

Abstract
This paper aims to show the process of Augmented Reality (AR) development representing existing building interior from a 3D sensor. These days, AR is becoming well known and widely used in various type of areas such as in gaming industry, medical training, and education. Due to its advantages, AR development for building interior is also necessity, specifically to assist people in understanding the scene more, in Architectural, Engineering, Construction / Facility Management (AEC/FM) applications as well as businesses like interior designing and household marketing. With respect to current situation of COVID-19 pandemic that has restricted travelling makes the usage of AR very beneficial and it could assist people to visualize the interior that needs to be visited. With the aid of suitable sensors like 3D sensor, the development of AR representing existing building interior which is usually full of clutter and occlusion could be eased.
An IoT Agricultural System For Harumanis Farm
Authors: Ismail F. A. , Ali Hassan M. K. , Ahmad Saad F. S. , Yazid H. , Aziz Safar M. J. , Basaruddin K.S. , Ismail A.H. , Ayob M.N. 
Published: December 2021

Abstract
Internet of Things (IoT) is a revolutionary technology that represents the future of communication and computing. The field of IoT implementation is vast and can be applied in every field. This project is about to develop an IoT system for Harumanis Farm as agriculture is becoming an essential growing sector throughout the world due to the increasing population. The major challenge in the Harumanis sector is to improve the productivity and quality of Harumanis without continuous manual monitoring. IoT improves crop management, cost-effectiveness, crop monitoring and also improves the quality and quantity of the crop. This IoT system completes with several sensors to monitor the Harumanis farm, such as temperature and humidity sensor, pH level sensor, soil moisture sensor, also nitrogen, phosphorous, and potassium (NPK) sensor. The system is a simple IoT architecture where sensors collect information and send it over the Wi-Fi network to the mobile applications.
A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning
Authors: Che Ali M A A , Ilias B , Abdul Rahim N , Abdul Shukor S A , Adom A H , Saad M A H 
Published: December 2021

Abstract
One of the stingless bee types named Heterotrigona Itama are widespread in the tropics and subtropics especially in Malaysia. Due to its excellent nutritional content, stingless bee honey has gained favour in recent years. According to some studies, stingless bee honey has been used to cure eye infections, open wounds, diabetes, hypertension, and a variety of other diseases. Additionally, this stingless bee is non-venomous and smaller in size than common bees. Nevertheless, beekeepers may encounter a number of obstacles that may result in colony failure and under-production. These problems can be attributed to a variety of factors such as surrounding temperature, surrounding humidity and predators. Numerous stingless bee colonies and other bee species lost in 2006 due to Colony Collapse Disorder as a result of this problem. Therefore, this article will review previous research on optimizing stingless beehive conditions via the use of the Internet of Things (IoT) and machine learning to minimise this issue. To begin, a review of existing research on the characteristics of stingless bees, particularly the Heterotrigona Itama species, has been conducted to understand the natural habitat of Heterotrigona Itama. Following that, the articles on colony division was reviewed in order to transition the colony from the conventional hive to the artificial hive which also reviewed its design from the past article to simplify the sensors installation, IoT monitoring system and honey harvesting. Then, the prior article on sensors and IoT deployment was examined to monitor and analysis the data online without disturbing the colony activity inside the beehives. Finally, the article on the application of machine learning with the beehive dataset was reviewed the most precise and accurate machine learning method to predict the existence of bee activity in the hives and the future condition of beehive.
An Aggressiveness Level Analysis Based On Buss Perry Questionnaire (BPQ) And Brain Signal (EEG)
Authors: Munian Lugieshwaran , Wan Ahmad Wan Khairunizam , Xu Tung Kai , Mustafa Wan Azani , Rahim Muhajir Ab. 
Published: December 2021

Abstract
Aggression is the most important human aspects that make daily things possible for individuals, to succeed and have a better level of behaviour. Aggression is feelings of anger or antipathy resulting in hostile or violent behaviour. The importance of aggression is to increase an individual dominance of the subject in their social environment. Traditionally, the subject’s aggression is usually measured by using a survey through Buss-Perry Questionnaire (BPQ). Considering the variability of the aggressiveness level, this study proposes investigation of aggression by using BPQ and Electroencephalography (EEG) to evaluate the aggressiveness level of the subjects. The results of the BPQ are analysed based on the final score that are responded by the subjects. In EEG experiment, the evaluation of subject’s aggressiveness while playing a smart phone game “Subway Surfers”, a basic method has been employed, namely correlation coefficient method. The EEG signals are recorded while the subject playing the game. The number of subjects involves in the experiment is 9 and they are the UniMAP’s male students at the age of 21-25 years old. In the analysis, the induced aggression is compared between BPQ with Net Aggressiveness Index (NAI), which is obtained from brain signals (EEG). The BPQ obtains the subjects #2 and subject #8 are the highest Buss-Perry Aggresinenes Index (BPAI) scores, which are 0.32244 and 0.32223 respectively. Meanwhile in EEG analysis the subject #8 only achieves the highest score of 0.34713. From the results of the investigation, it could be concluded that the use of EEG to identify the aggressiveness level will overcome the disadvantage of the conventional methods.
Cost of Energy Losses for Distributed Generation Using Hybrid Evolutionary Programming-Firefly Algorithm
Authors: Husni Noor Najwa Husnaini Mohammad , Rahim Siti Rafidah Abdul , Adzman Mohd Rafi , Hussain Muhammad Hatta , Musirin Ismail 
Published: December 2021

Abstract
The cost of energy losses analysis for distributed generation (DG) is presented in this paper using a Hybrid Evolutionary Programming-Firefly Algorithm (EPFA). The proposed method was created to determine the optimal DG sizing in the distribution system while accounting for the system’s energy losses. This study presents an investigation into hybrid optimization techniques for DG capabilities and optimal operating strategies in distribution systems. The objectives of this study were to reduce the cost of energy losses while increasing the voltage profile and minimize distribution system losses. In this study, the analysis was done by consider DG type I which is DG-PV. The suggested methodology was tested using the IEEE 69-bus test system, and the simulation was written in the MATLAB programming language. Power system planners can use appropriate location and sizing from the results obtained for utility planning in terms of economic considerations. From the simulation, the result shows the proposed method can identify the suitable sizing of DG while reduce cost of energy losses and total losses in the system.