bluuebunny/crossref_metadata_2025_split huggingface dataset:
Click Fraud Detection Approaches to analyze the Ad Clicks Performed by Malicious Code
Authors: Bathula Mahesh , Tanguturi Rama Chaithanya , Madala Srinivasa Rao 
Published: December 2021

Abstract
Mobile PR is an important component of the mobile app ecosystem. A major threat to this ecosystem’s long-term health is click fraud, which involves clicking on ads while infected with malware or using an automated bot to do it for you. The methods used to identify click fraud now focus on looking at server requests. Although these methods have the potential to produce huge numbers of false negatives, they may easily be avoided if clicks are hidden behind proxies or distributed globally. AdSherlock is a customer-side (inside the app) efficient and deployable click fraud detection system for mobile applications that we provide in this work. AdSherlock separates the computationally expensive click request identification procedures into an offline and online approach. AdSherlock uses URL (Uniform Resource Locator) tokenization in the Offline phase to create accurate and probabilistic patterns. These models are used to identify click requests online, and an ad request tree model is used to detect click fraud after that. In order to develop and evaluate the AdSherlock prototype, we utilise actual applications. It injects the online detector directly into an executable software package using binary instrumentation technology (BIT). The findings show that AdSherlock outperforms current state-of-the-art methods for detecting click fraud with little false positives. Advertisement requests identification, mobile advertising fraud detection are some of the keywords used in this article.
Machine Learning Medical Resource Allocation
Authors: Sailaja M. , Ahad Abdul , Sivaramakrishna K , Hussain Ali 
Published: December 2021

Abstract
In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes to assign health resources, and proposes a number of new ways to use these resources at the time of artificial intelligence in order to make human life part of this process and explore the good conditions which are shared by both the medical and computer industries.
Data analytics for traffic flow prediction in Custom using Long Short Term Memory (LSTM) networks
Authors: Pin Loon Lee , Refaie Elbaraa , Mohd Faudzi Ahmad Athif 
Published: December 2021

Abstract
This paper proposes data analysis for traffic flow prediction of customs to help the officer in Customs, Immigration, and Quarantine (CIQ) Complex to understand more about the traffic situation in CIQ. Currently in CIQ, the traffic behaviour for car is unpredictable; sometimes the traffic is very heavy while there are times where all the lanes are cleared. There is a plan to have installation of cameras for smart traffic management system in the future. Therefore, this research aims to have prediction of traffic flow based on time and visualize the trend of traffic data for the officer. The data consist of traffic flow and the respective timestamp. To analyse it with time-series data, Long Short-Term Memory (LSTM) Recurrent Network is used as deep learning approach for prediction. The data pre-processing and training of model would be done using Python. To organize the data, Tableau Prep Builder is used and integrate with Python to publish the data to Tableau Server for storage. An interactive dashboard would be designed on Tableau and made available online for the usage of the officer.
Visual tracking using multi-layer appearance approach
Authors: Abu Hassan Mohd Fauzi , Pri Azurahisham Sah , Ahmad Zakiah , Azahar Tuan Dir Tengku Mohd 
Published: December 2021

Abstract
This paper investigated single target tracking of arbitrary objects. Tracking is a difficult problem due to a variety of challenges such as scale variation, motion, background clutter, illumination etc. To achieve better tracking performance under these severe conditions, this paper proposed covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of centre location error (in pixels) compared to covariance descriptor with using a fixed bounding box. From this work, it is believed that we have constructed a great solution in choosing best layer for this descriptor. This will be addressed in the next future work such as consider target motion during tracking.
Validation of the Effectiveness of Near-Infrared Spectroscopy for Detecting Impurities in Milk Powder using ANN and SVM
Authors: Syahir A.S Fathinul , Basah S.N. , Yazid H , Aziz Safar M.J. , Ali Hassan M.K. 
Published: December 2021

Abstract
Nowadays, there are so many productions on the market that based on dairy product that include milk powder. The milk powder has different categories which is for children, adult and others. The milk powder involves the local product until the import product. The transparency of the content in the milk powder cannot be identified easily and can cause food poisoning to be happened. Hence, with the use of near-infrared spectroscopy, NIRS it can be use easily to detect the content in the food simply. There are many methods in NIRS which is reflectance and absorbance. This method being uses to detect which milk powder is adulterated. Ocean Optic XR-1 ES, USA device and Ocean View software have been used in this project to take data from the sample. The device use wavelength between 200nm-1025nm. The milk powder obtained from the supermarket and nearby stores. Data that have been taken then transferred into suitable table and will go through two different analysis method which is SVM and ANN. The reflection method is not suitable to detect the adulterated milk powder. From the result ANN shows the higher accuracy to classify the pure and Adulterant milk powder.
Development of aquaculture water quality real-time monitoring using multi-sensory system and internet of things
Authors: Abdullah A H , Saad F S A , Sudin S , Ahmad Z A , Ahmad I , Abu Bakar N , Omar S , Sulaiman S F , Che Mat M H , Umoruddin N A , Johari B H 
Published: December 2021

Abstract
Water quality is an important parameter for the health and growth of aquatic species in aquaculture farming system. The threshold values of the water main parameters should be monitored continuously. Contaminated aquaculture water will affect the health, growth and ability of animals to survive. In addition, it will also affect the harvesting yields based on the number and size of the animals. To overcome this problem, the main water parameters, namely temperature, pH, Dissolved Oxygen and Electrical Conductivity are monitored in real-time using a multi-sensory system and the internet of things. Data is acquired by a developed instrument and transmitted wirelessly via a GPRS / GSM module to a web server database. The data obtained are analyzed and monitored through the website and in real-time. Therefore, corrective action could be taken immediately for contaminated water, indicated by water parameters out of range. The system also provides an early signal to farmers based on a specific range of water quality parameters values. This will help farmers make adjustments to ensure appropriate water quality for the aquaculture system.
A study of extreme learning machine on small sample-sized classification problems
Authors: Ooi Boon Pin , Rahim Norasmadi Abdul , Masnan Maz Jamilah , Zakaria Ammar 
Published: December 2021

Abstract
Extreme learning machine (ELM) is a special type of single hidden layer feedforward neural network that emphasizes training speed and optimal generalization. The ELM model proposes that the weights of hidden neurons need not be tuned, and the weights of output neurons can be calculated by finding the Moore-Penrose generalized inverse method. Thus, the ELM classifier is suitable to use in a homogeneous ensemble model due to the untuned random hidden weights which promote diversity even with the same training data. This paper studies the effectiveness of the ELM ensemble models in solving small sample-sized classification problems. The research involves two variants of the ensemble model: the normal ELM ensemble with majority voting (ELE), and the random subspace method (RS-ELM). To simulate the small sample cases, only 30% of the total data will be used as the training data. Experiment results show that the RS-ELM model can outperform a multi-layer perceptron (MLP) model under the assumptions of a Friedman test. Furthermore, the ELE model has similar performance as an MLP model under the same assumptions.
Design of Remote Warning System for Miniature Circuit Breaker (MCB) Power Shortage via Internet of Things (IOT)
Authors: Quan L H , Hasan M Z , Khalid N S 
Published: December 2021

Abstract
Miniature Circuit Breaker (MCB) is an electro-magnetic device designed to cut off the circuit when an overcurrent occurs. This MCB system is essential in many fields that involved with electric and commonly found in industry, it will protect the electric component in the circuit from short circuit. The Internet of Things (IoT) technology is a new profound technology which will lead to a better and easy lifestyle world. One of the benefits of IoT system is the worker can monitor their work from home. With this new and promising technology, most of the work can be done at home by using mobile phone or a computer. As the title of the project presented, the project developed a microcontroller to monitor the MCB using the technology of the Blynk as the IoT platform. The system will send a message to the user to inform about the trips through a mobile phone by the Blynk application. At the same time, the alternating current (AC) of the MCB is record and graph at the mobile application.
Measurement of Negative Lightning Return Strokes Using a Proposed Small-Scale Parallel Plate Antenna at the Central Region in Peninsular Malaysia
Authors: Haris F Abdul , Kadir M Z A Ab , Sudin S , Johari D , Hamzah M N 
Published: December 2021

Abstract
Lightning can occur between the clouds (intra-clouds), ground-to-cloud (CG), and inside the clouds. The lightning strike hazards can be managed appropriately using a lightning detector system consisting of an antenna, buffer amplifier circuit, and a measurement device. In this study, the development and fabrication of small-scale parallel plate antenna were carried out by reducing the physical height and antenna dimension to measure the generated electric fields. The experimental work was conducted at the rooftop of the College of Engineering, Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, from August 2019 to March 2020. The total number of 115 return strokes (RS) of negative lightning events were recorded during the measurement period. Characterization of seven types of criteria for negative return stroke has been analysed and compared to the existing parallel plate antenna with a similar climate condition and different countries. Based on the comparative study, the proposed and the existing parallel plate antenna shows a good agreement. Hence, the proposed small-scale parallel plate antenna can be used as a portable, lightweight, and easy to install device for the lightning measurement system.
Intelligent Shopping Trolley (IST) - a social distancing device during pandemic shopping
Authors: Darren Low Chin Sheng , Assyakirin  , Amiruddin Ahmad , Tan Kheng Chia , Razak Sabrina , Kiang Lam Chee 
Published: December 2021

Abstract
Intelligent Shopping Trolley (IST) is a device that was built to help in fighting the Covid-19 pandemic. This Intelligent Shopping Trolley is equipped with a RFID and timer system, Indoor Positioning System (IPS) and Microwave Sensor Detection system to determine the distance and to alert customers as they shop in a supermarket. This Intelligent Shopping Trolley will also utilise the Internet of Things (IoT) to manage its functionality. The Intelligent Shopping Trolley will help customers to determine the distance between other customers while implementing social distancing measures that is recommended by the Ministry of Health Malaysia and manage their shopping time as well. Besides that, it helps supermarkets to track their customers after the time limit given to the customers ends. This paper explains the details on this Intelligent Shopping Trolley project. This project helps in making the community aware of the importance of social distancing.
Development of surveillance drone based internet of things (IoT) for industrial security applications
Authors: Ali H , Hang L Y , Suan T Y , Polaiah V R , Aluwi M I F , Zabidi A A Mohd , Elshaikh M 
Published: December 2021

Abstract
Drones or mini-unmanned aerial vehicles, have becoming an emerging trends due to their boundless applications in surveillance, military and numerous public services. Nowadays, deployment of surveillance drone for monitoring or security application remains challenging and ongoing research. As Internet of Things (IoT) becomes more commercialized, various concept of IoT have been integrated with the drones due to efficient usage. Therefore, this paper proposed the development of surveillance drone system based on IoT for industrial monitoring-security applications. The rationale of integrating IoT with surveillance drone is that it allows authenticated users to login from any device, anywhere, and view video or images from surveillance drones in real-time for security awareness. In this work, the surveillance drone consists of mechanical system, electrical and electronic interfacing and IoT platform (mobile application system). In electronic system, power module, communication module, sensor and actuator as well as user interface module have been adopted and integrated into the systems. Besides, in software development system, user interface configuration was developed through mobile application to serve as IoT platform. A series of experiments shows that the surveillance drone based IoT able to operate with a promising flying distance with surveillance camera as the “eyes” of the drone system.
Check-In Location System Using NFC Technology
Authors: Chze Ooi Nian , Markom Marni Azira , Hui Ong Xin , Chin Wong Tze , Vern Koay Yu , Tan Erdy Sulino Mohd Muslim , Nordin Noradila 
Published: December 2021

Abstract
In COVID 19 pandemic, new norms have been introduced, including, to leave a record when checking-in to a particular place. This new norm is regulated in order to trace locations that have been visited by someone with positive COVID-19. This paper presents a work on development of check-in location system. The system implemented Near Field Communication (NFC) technology which is mainly utilized two NFC compatible devices where an identification card (IC) is used as a smart object (NFC tag) and the NFC detector as an NFC reader to exchange information. Testing has been conducted in order to observe the system performance, and, the results showed that this system is able to collect information of users who were coming to premise. Also, the information can be checked by authority in order to track someone with positive COVID-19. As conclusion, this system can be an alternative to MySejahtera App.
Automotive Mechanical Vehicle Starter
Authors: Kader M M M. A , Razali Z B , Mustafa W A , Saidi S A , Nagoor Gunny A A , Setumin S , Osman M K , Idris M , Akbar M F , Sarbini M A A , Mizam N S S 
Published: December 2021

Abstract
This research is used to crank start automotive vehicle. There are many different system used in order to start-up vehicles using electric starter, in the time of battery low-power or totally drained. The purpose of this research is to help the driver to get out of this difficulty. Nowadays there are many people that have experienced such a bad moment, where they are stranded at road side due to malfunction starter in their car because of battery problem. Most of the vehicle electric starter failure is because of battery corrosion or battery undercharged. The importance of this research is to solve this problem. Starter is a vital part of the vehicle, without it no automotive vehicles able to operate. These starters will rotate an internal-combustion engine to initiate the engine’s operation under its own power. Starters also can be malfunction too due to corroded electrical connections or an undercharged battery. This system can be used to solve this problem. This system used human energy by using mechanical parts in order to produce electrical power. In order to produce electrical current, workforce will be applied by rotating the wheel that already linked by belt and from that rotations will trigger a magnetic force and it will produce an electrical current and supply it into battery. This system is divided into two development; hardware development and software development. The hardware development involved, mechanical device which is used and electrical device such as monitor. For software development, Fritzing is used to construct circuit.
Automated Negative Lightning Return Strokes Classification System
Authors: Haris F. Abdul , Ab Kadir M.Z.A. , Sudin S. , Johari D. , Jasni J. , Mohammad Noor S.Z. 
Published: December 2021

Abstract
Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes.
Robot Face and Its Integration to the Mobile Robot for Wireless Signal Collection in the Fingerprinting-Based Indoor Positioning System
Authors: Sarhan M A H , Ismail A H , Ayob M N , Hashim M S Mohd , Azmi M S M , Basri H H , Othman S M , Safar M J A 
Published: December 2021

Abstract
The wireless data collection for instance the Received Signal Strength (RSS) of the Wireless Fidelity (Wi-Fi) remained unfavourable in the Indoor Positioning System utilizing the signal fingerprinting approach. This is because the enormous sampling time and routines works making it tedious human labour. To alleviate this issue, we propose to use a robot for wireless data collection. The robot, named ‘ICSiBOT’ is a service robot with multiple purpose such as assisting human in daily lives, guest or hospitality robot and man others. This paper mainly describes the ICSiBOT robot face with speech recognition technology and the integration of the robot face to the motion controller. The experimental was conducted to see the correlation between the synthesized instructions from the speech in terms of distance need to be travelled i.e., the location for wireless signal collection and translate them into actual distance travelled. The results showed that the robot is able to travel to the specific distance as instructed to the robot face.
Markov Decision Process approach in the estimation of raw material quality in incoming inspection process
Authors: Mani Annapoorni , Bakar Shahriman Abu , Krishnan Pranesh , Yaacob Sazali 
Published: December 2021

Abstract
The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtime due to defective 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 raw material acceptance prediction model (RMAP) developed based on the Markov analysis. RFID tags are used to track the parts throughout the process. A secondary dataset can be derived from the raw RFID data. In this study, a dataset is simulated to reflect a typical incoming inspection process consisting of six substations (Packaging Inspection, Visual Inspection, Gauge Inspection, Rework1, and Rework2) are considered. The accepted parts are forwarded to the Pack and Store station and stored in the warehouse. The non-conforming parts are returned to the supplier. The proposed RMAP model estimates the probability of the raw material being accepted or rejected at each inspection station. The proposed model is evaluated using three test cases: case A (lower conformities), case B (higher conformities) and case C (equal chances of being accepted and rejected). Based on the outcome of the limiting matrix for the three test cases, the results are discussed. The steady-state matrix forecasts the probability of the raw material in a random state. This prediction and forecasting ability of the proposed model enables the industries to save time and cost.
Categorization of material quality using a model-free reinforcement learning algorithm
Authors: Mani Annapoorni , Bakar Shahriman Abu , Krishnan Pranesh , Yaacob Sazali 
Published: December 2021

Abstract
Reinforcement learning is the most preferred algorithms for optimization problems in industrial automation. Model-free reinforcement learning algorithms optimize for rewards without the knowledge of the environmental dynamics and require less computation. Regulating the quality of the raw materials in the inbound inventory can improve the manufacturing process. In this paper, the raw materials arriving at the incoming inspection process are categorized and labeled based on their quality through the path traveled. A model-free temporal difference learning approach is used to predict the acceptance and rejection path of raw materials in the incoming inspection process. The algorithm presented eight routes paths that the raw materials could travel. Four pathways correspond to material acceptance, while the rest lead to material refusal. The materials are annotated using the total scores acquired in the incoming inspection process. The materials traveling on the ideal path (path A) get the highest total score. The rest of the accepted materials in the acceptance path have a 7.37% lower score in path B, whereas path C and path D get 37.28% and 42.44% lower than the ideal approach.