 Subjects -> ELECTRONICS (Total: 207 journals)
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- Factors Affecting the Adoption of Secure Software Practices in Small and
Medium Enterprises that Build Software In-house
Authors: Wisdom Umeugo, Kimberly Lowrey, Shardul Pandya
Pages: 1 - 7
Abstract: Software has grown enormously in value because of its wide use for domestic, public, and economic activities. Software must be secure because exploited software vulnerabilities can negatively affect individuals’ and organizations' financial, health, and economic well-being. Various authors recommended practicing a secure software development lifecycle (SSDLC) to ensure that software is released secured. Software small and medium enterprises (SMEs), the dominant software publishers, have not widely adopted the SSDLC. This study approached the problem of software SMEs’ inadequate adoption of SSDLC from an innovation adoption perspective based on the diffusion of innovation theoretical framework (
DOI ). Five
DOI factors, relative advantage, compatibility, complexity, trialability, and observability, were assessed for significance to software SMEs’ intention to adopt SSDLC. A random sample of 200 participants from a population of software security decision-makers of software SMEs based in the United States that develop software in-house were surveyed via an online close-ended questionnaire. Relative advantage, compatibility, and trialability were statistically significant to SME SSDLC adoption intention. Complexity and observability were not statistically significant to SME SSDLC adoption intention. Trialability was the strongest predictor of SME SSDLC adoption intention. SME software security decision-makers may find that the results of this study help to determine the factors they should consider when deciding to introduce the SSDLC into their software development process. The result of the study has implications for practice and social change because increased SME SSDLC adoption potentially results in the development of more secure software and fewer software security incidents.
PubDate: 2023-04-20
Issue No: Vol. 14, No. 2 (2023)
- An Artificial Neural Network-Based Security Model for Face Recognition
Utilizing Haar Classifier Technique
Authors: Amit Mishra
Pages: 8 - 16
Abstract: A facial recognition system is a computer program that uses a digital image or a video frame from a video source to automatically recognize or confirm a person. An amicable approach to achieving the desired result in facial By comparing certain facial traits from the image with a facial database, biometrics. This paper developed an ANN-based secure facial recognition model that will accurately and efficiently record all data and information about an individual. This system uses Haar Classifier Technique; face detection algorithms, Opencv, Visual C++, Haar like Features and the Canny Edge Detection and OPenCV. The results therefore demonstrate that the system successfully allows user to login using credentials, enrols, registers, logs and save captures data and facial biometric. The system authenticates by analysing the upper position of the two eyebrows vertically. The method searches from w/8 to mid for the left eye and from mid to w - w/8 for the right eye. Thus, w denotes the image's width, while mid designates where the two eyes are cantered. The black pixel-lines are vertical and are positioned between mid/2 and mid/4 for the left eye and mid+(w-mid)/4 and mid-+3*(w-mid)/4 for the right eye. The height of the black pixel-lines is measured from the eyebrow starting height to (h- eyebrow starting position)/4.
PubDate: 2023-04-20
DOI: 10.26483/ijarcs.v14i2.6952
Issue No: Vol. 14, No. 2 (2023)
- Assessing Long-term Impacts of Disaster Using Predictive Data Analytics
for Effective Decision Support
Authors: shailendra Kumar Mishra
Pages: 17 - 25
Abstract: Disaster is a big issue that seriously disrupts and affects the community or society. The impact of a disaster causes a short and long period of time. To analyze the impacts of disasters there are lots of available related datasets. Data analytics methods have the potential to assess the impacts of different types of disasters. Collectively data analytics and machine learning techniques play an important role in transforming and being able to make decisions about our social, economic, mental, and psychological things. The objective of this paper is to assess the impacts of disasters from immediate term to long-term, provide crucial help to the emergency management workforce, and policy decisions making based on the latest available datasets. With the help of the various data agencies, extraction of information and activities carried out, we can determine the effects on disaster victims, their community and impacts on society in general. The analysis provides the statistics that can guide our emergency service about the status of facilities that can further support the survivors, and other related information. Detailed assessment i.e., structural survey and hazard mapping provide specific information about reconstruction and mitigation to monitor the situation, needs of the victims, and supporting entities. The assessment is based on the type of disaster that happened and its impact after a few years.In the current technological advancement of data analytics and machine learning algorithms, the prediction of the long-term effects of a disaster can be performed. Analyzing the impacts over a long period of time is also dependent on the growth of actively cared datasets gathering bodies like agencies, government, NGOs, media, etc., where prediction of short-term and long-term impacts is dependent on the available datasets. Available datasets are preprocessed using data analytics tools and implementation of training and testing for the purpose of predictions and recommendations. As a huge amount of data sets are available through different sources the classification of the datasets can also be performed resulting fast and accurate processing. Model validation techniques play an important role to check the validation, test result, and related outcomes. In this paper advanced machine learning and data analytics tools i.e., XG boost, modified SVM, and modified RF are used for better prediction. The analysis of the short-term effects of disasters has already been suggested and recommended by the various conventional approaches. Here the focus is to analyze and detect the long-term effects of a disaster along with recommendations and models preparing for good decision-making. Therefore, planning should be focused on assessing the impacts from short-term to long-term. The findings of the paper would be helpful to the agencies, local & national authorities, and the government by recommending action plans and their future effects for a longer period in case of disaster.
PubDate: 2023-04-20
DOI: 10.26483/ijarcs.v14i2.6956
Issue No: Vol. 14, No. 2 (2023)
- A comparative investigation of e-learning with traditional learning
Authors: Archana Thakur
Pages: 26 - 28
Abstract: E-learning based methods provide vital teaching methods for more than a decade. E-learning provides learners to have interactive e-content that is full of multimedia. It has been confirmed that it has a tremendous impact on the process of learning. The present work focuses upon a comparative examination of e-learning based methods with traditional teaching methods. The comparative examination performed on different user groups shows the preference of e-learning based methods over traditional teaching methods. The feedback regarding the two methods is collected from various user groups viz. students, researchers, instructors and staff. E-learning based methods provided outstanding feedback as compared to traditional teaching methods.
PubDate: 2023-04-20
DOI: 10.26483/ijarcs.v14i2.6958
Issue No: Vol. 14, No. 2 (2023)
- Automatic Question Generation System Based Natural Language Processing
Using Python
Authors: Selvakani Kandeeban, K.Vasumathi Sundaresan, K. Dinesh Kumar
Pages: 29 - 32
Abstract: Natural Language Processing has seen a surge in research on Automatic Question Generation (AQG) in recent times. AQG has proven to be an effective tool for Computer-Assisted Assessments by reducing the costs of manual question construction and generating a continuous stream of new questions. These questions are usually in the format of "WH" or reading comprehension type questions. To ensure natural and diverse questions, they must be semantically distinct based on their assessment level while maintaining consistency in their answers. This is particularly crucial in industries like education and publishing. In our research paper, we introduce a novel approach for generating diverse question sequences and answers using a new module called the "Focus Generator". This module is integrated into an existing "encoder-decoder" model to guide the decoder in generating questions based on selected focus contents. To generate answer tags, we employ a keyword generation algorithm and a pool of candidate focus from which we select the top three based on their level of information. The selected focus content is then utilized to generate semantically distinct questions.
PubDate: 2023-04-20
DOI: 10.26483/ijarcs.v14i2.6961
Issue No: Vol. 14, No. 2 (2023)
- Performance analysis of routing protocol in mobile ad-hoc networks
Authors: jogendra kumar
Pages: 33 - 38
Abstract: Routing in a mobile ad hoc network (MANET) is a difficult task due to the constantly changing network topology and the absence of a fixed infrastructure. In such a scenario, mobile hosts can act as both hosts and routers, forwarding packets for other mobile nodes in the network. Routing protocols used in MANETs must be able to adapt to frequent changes in topology, while minimizing the impact on wireless resources. The AODV, DSR, ZRP and DYMO protocol are specifically designed for mobile nodes in wireless multihop ad hoc networks. It is capable of adapting to the changing network topology and determining unicast routes between nodes within the network. This paper presents a comparative analysis of commonly used routing protocols in terms of key performance metrics, including Broadcast sent packets, CTS packets sent, Packet drops due to re-transmission,RTS re-transmission the timeout , Unicast packet received ,RTS packets sent,Packet due to ACK timeout, ACK packet sent, Unicast sent packet and Broadcast Packet Received. The study's findings demonstrate that the routing protocols' performance is influenced by the network's size, node density, and mobility patterns. These routing protocols showing the simulation performance using random waypoint model on qualnet simulator.
PubDate: 2023-04-20
DOI: 10.26483/ijarcs.v14i2.6962
Issue No: Vol. 14, No. 2 (2023)
- MERN STACK-BASED CAR RENTAL WEBSITE DEVELOPMENT
Authors: Swastik Chandrashekhar Bakale, Archana Monish Naware; Sneh Vilas Parab, Shrishty Anil Saxena, Sameep Atul Anjaria
Pages: 39 - 44
Abstract: The Car Rental System is a web-based platform designed to provide a user-friendly interface and a seamless rental experience. It is built using the MERN stack, which ensures fast and responsive user interfaces and efficient processing of customer requests and data. The platform includes a payment gateway for secure and swift payment processing, enabling efficient rental transactions. With the increasing demand for car rental services due to the rise of tourism and ride-sharing services, the platform offers a convenient and efficient solution. The system's advanced functionality enables fast retrieval and management of customer data, along with filtering capabilities to facilitate easy navigation and car listing. By integrating a geo-location API, the platform allows customers to find rental cars quickly and easily. MongoDB serves as the platform's database to store and manage customer information, vehicle information, and rental data. The system's flexibility allows renters and rentees to add and delete cars as per their requirements, reducing the time and effort required to rent a car. Overall, the Car Rental System provides a reliable and efficient solution for car rental companies to offer customizable rental experiences to their customers.
PubDate: 2023-05-01
DOI: 10.26483/ijarcs.v14i2.6971
Issue No: Vol. 14, No. 2 (2023)
- A SURVEY ON PREDICTION OF AUTISM SPECTRUM DISORDER USING DATA SCIENCE
TECHNIQUES
Authors: RAMYA RAJAGOPAL
Pages: 45 - 50
Abstract: Autism Spectrum Disorder is a lifelong brain developmental disorder. Diagnosing the level of Autism and predicting the severity of the same are too complex, and it requires a depth analysis of the historical data on the autism patient. Nowadays, Data science techniques play a vital role in diagnosing autism. Decision Tree, Random Forest, Logistic Regression, Adaboost, Naïve Bayse, K-Nearest Neighbour, Support Vector Machine and etc., are the few techniques labeled under the roof of data science are used to predict such disorders. The paper aims to present a survey on the various models proposed by various researchers to predict the severity of autism using data science techniques.
PubDate: 2023-05-04
DOI: 10.26483/ijarcs.v14i2.6969
Issue No: Vol. 14, No. 2 (2023)
- PREDICTION OF AIR POLLUTION AND AN AIR QUALITY INDEX USING MACHINE
LEARNING TECHNIQUES
Authors: L. Ramesh, S. Gopinathan
Pages: 51 - 55
Abstract: Air pollution is the “world’s largest environmental health threat”[1], causing 7 million deaths[1] worldwide every year. Its major constituents are PM2.5, PM10 and the harmful green house gases S02, N02, C0 and other effluents from vehicles and factories affecting not only humans but also other living organisms both on land and sea. The only effective solution to this global issue is to implement machine learning algorithms to predict the AQI (Air Quality Index) that can make the people aware of the condition of the air of a certain region such that certain actions could be issued by the government for the improvement of the air quality in the future. The prime objective behind this project is to predict the AQI based on the concentration of PM2.5, PM10, S02, N02, C0 as well as weather conditions like temperature, pressure and humidity [2].Hence the data set is combined from various web sources like cpcb and uci repository in order to bring accuracy in the prediction and to justify whether the Quality of air is suitable or not. This prediction will be brought about with the help of some supervised machine learning algorithms and the observation and the result will state which algorithm is giving better accuracy in prediction of AQI and which one is giving less error.
PubDate: 2023-05-04
DOI: 10.26483/ijarcs.v14i2.6972
Issue No: Vol. 14, No. 2 (2023)
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