Asian Journal of Research in Computer Science
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Selective Breeding under a Hierarchical Mating Using Osborne Index Web App
No full textThe poultry industry has targets to meet consumption trends and thus to produce genetically superior birds with high productivity of egg. Better egg production techniques are recommended, to satisfy in-house and export demand. The correlation of egg production with various parameters is considered by various breeders. With the efforts of breeders to satisfy demand, poultry breeding has introduced individual feed conversion testing, Osborne index, pedigreeing, hybridization, selection index, artificial insemination, and mass selection etc. The most reliable and proven Osborne index states that the maximum efficiency of egg production can be obtained by selection on the basis of a combination of family average and individual record. Technological advances have fostered the poultry sector in the last few decades. ICT led transmutation of processes and practices is apparent in almost all aspects of human activities. Knowledge about a particular breeding technique is required for its prefect implementation. These techniques require a kind of data mining and statistical analysis for matting sires and dams. In the era of 5G, Web Apps can provide better options for providing timely, precise analyzed information to poultry owners or breeders. This paper proposes a device responsive web app for Osborne Index for hierarchical mating using selective breeding
Monitoring and Control System for Precision Agriculture Using Wireless Sensor Network
No full textAgriculture is an essential part of the Pakistani economy, and it is embedded into the country’s antiquated financial framework. Intelligent systems can be developed through the usage of IoT-based technology. In hydroponics farming, precision is a challenge, particularly for some sensitive plants like bok choy and lettuce. These plants need a certain amount of fertilizer and water each time in order to grow as well as possible. The Internet of Things (IoT) enables routine monitoring of every element of a person\u27s life. A remedy might be to periodically evaluate the plants\u27 nutrient and water requirements. In this study, monitoring and control systems for hydroponic precision agriculture are developed using IoT and fuzzy logic. Fuzzy logic is utilized to control how much food and water the plants receive, and IoT is used to make it feasible to periodically assess the nutritional and water needs of plants
Refinement of Voting System through Visual Cryptography and Multi-factor Authentication to Further Mitigate Clone Phishing Attack
No full textBackground: The growing concern over phishing attacks on voting systems, particularly with the rise of online voting, has highlighted vulnerabilities in elections. The convenience of internet voting has led to security risks like clone phishing attacks. While online voting offers accessibility benefits, security, privacy, and usability issues have arisen. Multi-factor authentication (MFA) has been proposed to enhance security in mobile internet voting systems. Visual cryptography, dividing images into shares for decentralized data storage, is suggested to counter clone phishing. MFA\u27s effectiveness in various sectors is established, and secure voting systems combining visual cryptography and blockchain have been proposed. This research sought to bolster the security of the voting system using visual cryptography and multi-factor authentication (MFA), with the goal of increasing voter trust and the reliability of the voting procedure, by designing a secure system and evaluating its performance, accuracy, and accessibility.
Methodology: The researchers developed an improved voting system using visual cryptography and multi-factor authentication, assessed its performance against Eligo and Voxvote, utilized Java for programming, MYSQL via XAMPP for the database, HTML, CSS, JavaScript, and PHP (Laravel) for client-server sides. The Scrum agile methodology was followed, employing brief sprints for adaptive development. Evaluation was done through web tools and benchmarks. The system\u27s architecture and flowchart were presented, featuring an interactive GUI, Java 2 platform, MySQL, PHP 7, and specific hardware/software requirements. System setup included database and server configuration, fingerprint scanner installation, and Java runtime setup. Deployment involved executing a built Java program, and system testing was conducted on Windows 10, utilizing the Apache server and initiating the "Electronic Voting" program for online multi-factor authentication.
Results: Achieving a performance rate of 92%, the developed system surpassed its competitors Eligo and Voxvote, which achieved scores of 15% and 33% correspondingly, as indicated by the data. Eligo and Voxvote attained scores of 88% and 80% individually, whereas the newly designed system obtained a rating of 93% in terms of accessibility. Nonetheless, the research also highlighted specific downsides, encompassing intricacy, reluctance to embrace change, and technological barriers. To tackle these issues and enhance the adoption of such systems, these limitations underscore the necessity for further investigation and advancement.
Conclusion: The study demonstrates that integrating multi-factor authentication and visual cryptography significantly enhances voting system security, reducing clone phishing risks. Visual cryptography secures decryption keys, preserving voting integrity, while multi-factor authentication adds defence against unauthorized access. The researcher\u27s online voting system outperforms competitors in performance metrics and accessibility. The study underscores the importance of combining these techniques for improved voting system reliability and security
Language Agnostic Ontology Extension Framework
No full textOntologies are powerful structures used to define the schema and organization of knowledge. They provide a framework for precisely and explicitly organizing concepts, entities, attributes, and relationships within a domain, enabling effective data management, knowledge sharing, and intelligent decisionmaking. Knowledge graphs, on the other hand, store data in a graphical form, facilitating semantically rich and interconnected data analysis, management, and exploration. Nodes represent entities, edges depict relationships between nodes, and attributes showcase node properties. Combining the strengths of ontology, which offers schema and concept-level modeling, with the data richness of knowledge graphs, one can unlock powerful reasoning and inference capabilities that closely resemble facts and truths. The symbiotic relationship between knowledge graphs and ontology, wherein ontology serves as a blueprint for representing knowledge in the graphs, creates a robust foundation for knowledge representation. Traditionally, ontologies have been created by experts or skilled teams with domain knowledge, aiming to build comprehensive ontologies supporting holistic data representation. However, these domain-specific ontologies require manual intervention for updates and remain language-specific, making it challenging to transfer them across different language formats. To address this challenge, a crucial component is an automatic generation framework for language-independent ontologies. Such a capability would provide a data-driven approach for creating necessary ontologies. The process involves taking a text corpus as input and subsequently constructing a knowledge graph through named entity recognition. This graph is then transformed into a concept-level modeling graph, where an ID-based approach is utilized to represent the concepts. The ID-based concepts can be extended to facilitate merging and updating the ontology with new relevant information. Concurrently, this approach can also be expanded to achieve language independence, enabling the conversion of the ontology to any required language. Expressing these ontologies in mathematical terms is essential to achieve language-agnosticism, ensuring completeness, relevance, and independence from specific domains, thereby making them applicable across various linguistic contexts
Classifying Bengali Newspaper Headlines with Advanced Deep Learning Models: LSTM, Bi-LSTM, and Bi-GRU Approaches
No full textReading newspapers is beneficial for people of all ages and the global community. The enjoyment of gathering diverse data from various sources adds to the overall experience. To enhance specificity in Bengali news headlines, recognizing the news genre becomes crucial. Recognizing the genre of the news, it is a very challenging task in Bengali Text Classification with the help of AI. A very few research works is done on Bengali News headline classification and we have done a model to provide a solution to the addressed issue. Due to the continuous change of the structure of the news headlines, we have employed a neural network adoption connection to our methodology experiment on a mixture of primary and secondary dataset. Achieving significant results, we implemented a Bengali dataset in Multi Classification using Long-Short Term Memory (LSTM), Bi- Long-Short Term Memory (Bi-LSTM), and Bi-Gated Recurrent Unit (Bi-GRU). The dataset is established by aggregating news headlines from various Bengali news portals and websites, showcasing robust categorization performance in the end product. Six categories were employed for the classification of Bengali newspaper headlines. The Bi-LSTM Model emerged with the highest training accuracy at 97.96% and the lowest validation accuracy at 77.91%. Furthermore, it demonstrated enhanced sensitivity and specificity
Comparing Unbalanced and Balanced CNTFET Ternary Adders and Multipliers with the Corresponding Binary Ones
No full textThis paper compares the performance of the ternary adders and multipliers using balanced and unbalanced set of values. We use the 1-trit adders to evaluate the two versions of a 4-trit propagate adder, which are comparedwith a 6-bit corresponding propagate adder. Similarly, we compare the two types of 2*2 trit multipliers with a3*3 bit multiplier. The simulations with a 32-nm Carbon Nanotuble Field-Effect Transistor (CNTFET) technology show that the binary adders and multipliers are more efficient than the ternary ones that compute the same amount of informatio
Comparison and Optimization of Energy Efficient Algorithm for Component Base Distributed Computing in 5G Networks
No full textIn the present scenario, the wired network or wireless networks is an application across the world. So the wire networks used in various software industries, educational institutions, and various enterprises used such as distributed data centers. The data transmission works like a flow of electricity in a linear way. So in this process during the data transmission exhibits from one stage to another the energy consumption in carbon footprint (i.e. CO2). The data transmission is two types of methods (1) Communication-Based (2) Component-Based. Here this paper concludes the compared study of component-based energy consumption using the Bellman-Ford Algorithm and Dijkstra\u27s Algorithm. The results and finding measurement as discussed in this paper
Detecting and Correcting Contextual Mistakes in Sentences Using Part of Speech Tags
No full textA grammar checker is a tool to check each sentence in a text to see whether it conforms to the grammar. In case it finds a structure that conflicts with the conformity to the grammar, it would give suggestions for alternatives. The grammar checkers for European languages and some Indic languages are well developed. However, perhaps, owing to Tamil being a morphologically rich and agglutinative language this has been a challenging task. An approach to detecting and correcting grammatical mistakes due to subject and finite-verb disagreement with regard to person, number and/or gender and due to disagreement in tense aspects in Tamil sentences is proposed in this paper. A method has been proposed that uses hierarchical part-of-speech tags of words to detect the grammatical mistakes in subject and finite-verb agreement and mistakes in tense aspects in Tamil sentences. Two sets of Tamil grammar rules are used to generate suggestions for the grammatical mistakes. Test results show that the proposed grammatical mistake detection and correction system performs well
Sentiments Analysis Study for the Adaptation of Online Medical Forums
No full textOnline medical forums allow users to research medical treatments or conditions and gain support from other users dealing with similar issues. These forums have become increasingly popular over the past decade, helping connect medical patients and professionals from various backgrounds and creating a supportive online community. This paper evaluates the adaptation of online medical forums in Nigeria, to analyse the opinions of Nigerian citizens in using the medical system. In this research, a Tweepy API (python library/ module that contains the required object and functions for managing the Twitter data), textblob (python library for processing textual data), and matplotlib modules (for creating statistical charts) were used to extract related tweets from the Twitter. The project involves steps like creating a Twitter developer account, which gives the privilege to create a Twitter application and has keys for accessing online resources. The analysis begins by searching for the data, storing it, filtering it and then returning the sentiment analysis to review the positive, neutral, and negative tweets. The output of this project returns a table and scatter graph that displays the Subjectivity and polarity of the opinions of Nigerians on the adaptation of online medical forums. Similarly, a bar chart is obtained that shows the positive tweets, the negative tweets and the neutral regarding online medical forums
Computer Network Optimization Using Queue Tree and Peer Connection Queue (PCQ) Method at SMK Negeri 1 Sumbawa for Learning Support
No full textBandwidth struggles and non-optimal internet networks can result in connection problems, such as slow connections and allowing users not to connect to the internet. therefore it is necessary to optimize computer networks to be able to maximize service to users, both used to find information, download and upload data. This study uses the Queue Tree and PCQ methods which aim to optimize computer networks at SMKN 1 Sumbawa Besar and improve the quality of computer networks in terms of delay, jitter, throughput, and packet loss. This type of research is qualitative and quantitative research. Research methods include data collection (observation, interviews, literature study), analysis, design, implementation, and testing. Based on the test results after using the Queue Tree and PCQ methods, the average values of delay, jitter, throughput, and packet loss are more optimal than before applying the Queue Tree and PCQ methods. The delay measurement in data rate before applying the Queue Tree and PCQ methods is 1414925 m/s and after applying the Queue Tree and PCQ average delay is 3,165 ms. Test Jitter on the data rate before applying Queue Tree and PCQ methods is 1414957 ms and after applying the queue tree and PCQ jitter is 3,164 ms, Throughput on data rates before applying the Queue Tree and PCQ methods is 5043 kbps and after applying the queue tree and PCQ throughput is 2486 kbps, Test packet loss on data rates before applying the Queue Tree and PCQ methods is 7,911% and after applying the queue tree and PCQ methods is 0,030%