Journal of Computers and Intelligent Systems

Next-Generation Wireless Networks

2024-03-02T17:16:54+00:00

Next-generation wireless networks, commonly referred to as 5G and beyond (6G, 7G), are poised to revolutionize the way we communicate, connect, and interact in our increasingly digital world. With unprecedented speed, capacity, and reliability, these networks are expected to deliver ultra-low latency and support massive machine-to-machine communications, enabling a wide range of innovative applications and services. In this work, we offer a survey of the principal methods now in use and architectural principles driving the development of next-generation wireless networks. It explores deep into topics like millimeter-wave communication, huge MIMO systems, and network virtualization to explain their core ideas and principles. Potential advantages and disadvantages of such advanced networks are discussed. Topics covered include the introduction of Internet of Things (IoT) devices, the development of edge computing, and the requirement for increased security and privacy safeguards. Furthermore, this paper highlights ongoing research efforts and standardization activities in the field, aiming to ensure seamless interoperability and global deployment of next-generation wireless networks. As the demand for ubiquitous connectivity continues to escalate, the transition to next-generation wireless networks promises to reshape industries, empower smart cities, and unlock the full potential of emerging technologies such as autonomous vehicles, virtual reality, and artificial intelligence.

Prediction Of Water Quality Using Effective Machine Learning Techniques

2024-05-13T08:43:33+00:00

One of the most vital natural resources for all earth's living things is water. Life's fundamental need is access to clean water. Water quality has substantially declined over the previous few decades as a result of pollution and numerous other problems. In this study, machine learning (ML) algorithms are developed to predict water quality and water quality classiﬁcation (WQC). For the prediction of water quality classification, six machine learning algorithms Naïve Bayes, Random Forest (RF), Gradient Boosting (GBoost), K-nearest neighbor (K-NN),Logistic Regression (LogR), and Decision Tree (DT), have been used. The models were evaluated based on 16 parameters. The machine learning model’s result demonstrates the Random Forest model out performed than the other models.

Agile Ontology Development: A Comprehensive Framework from Preliminary Investigations to Evaluation

2024-05-24T13:10:37+00:00

This research proposes a comprehensive framework for ontology development within the database domain, integrating agile methodologies from initial investigations to evaluation. The framework comprises three main phases: Preliminary Investigations, Ontology Design and Development, and Ontology Evaluation, each encompassing specific sub-stages. In the Preliminary Investigations phase, entity recognition is conducted through interviews, document analysis, and keyword extraction using tools like Monkey Learn. Relationships between entities are identified to establish hierarchies and associations, facilitating semantic representation. The Ontology Design and Development phase involves domain modeling and ontology implementation, including the transformation of entity relations into OWL classes and properties. Guidelines for mapping entity relations to OWL are provided, ensuring a seamless transition from Entity Relation Diagrams (ERD) to ontology representation. Finally, the Ontology Evaluation phase employs two methods: Domain Experts Evaluation and Pellet Reasoner. Domain experts assess the ontology's credibility, consistency, completeness, and conciseness, while the Pellet Reasoner ensures logical consistency and provides essential inference services. The proposed framework offers a systematic and agile approach to ontology development, particularly beneficial in dynamic domains like database management.

Application Layer Issues and Challenges in Supply Chain 4.0

2024-05-23T04:35:52+00:00

Computer networks have improved throughout time and are now more suited to meet contemporary demands, but this evolution has also led to a rise in network risks and assaults. As computer networks aid in the secure and effective movement of data, they also store sensitive information that might be useful to persons and groups outside the network who would want to steal it and use it for their own ends. This might lead to serious network security concerns and vulnerabilities. Supply Chain 4.0 is one of the most emerging phenomena derived from the IR 4.0 that includes the applications of (Internet of thing) IoT, Wireless Sensor Networks (WSN’s) and robots to collectively work and develop a productive environment. Supply chain was mostly a manual and hectic process but with the modern communication and network advancements it is being introduced to more extensive digitization where IoT and advances networking approaches are being carried out. This article highlights the majority of issues, challenges of the application layer in supply chain 4.0. Moreover, the respective attacks targeting specific application layer protocols are stated in detail along with the limitations of the current work with the required research development is also mentioned.

Resume Ranking Using Natural Language Processing

2024-05-24T14:15:32+00:00

Finding the ideal candidate for a position is one of a company’s most important and crucial tasks. The conventional approaches typically necessitate spending a significant amount of time manually going through each applicant’s application, reviewing their resumes, and compiling a shortlist of candidates who ought to be contacted for an interview. Numerous resumes are received by companies, many of which are poorly formatted. On the other hand, selecting a candidate based on their resume has not yet been completely automated. The applicant will be able to upload their pdf resume on our website. We will use Natural Language Processing to rank abilities and work insight from the unstructured resumes. Our model will rank the best candidate in each category. The process of screening is made easier by the removal of all irrelevant information, and recruiters are able to better analyze each resume in less time.