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We propose an enhanced variant of the traditional Fuzzy C-Means (FCM) algorithm tailored for leveraging neighbourhood information in non-image datasets residing in Euclidean space. Our novel methodology aims to capitalize on spatial contextual cues inherent in such datasets, thereby complementing the inherent fuzziness of individual data points. Through the incorporation of neighbourhood information, our approach extends beyond the limitations of conventional FCM, leading to improved clustering performance. We validate the efficacy of our method using synthetic and real datasets, demonstrating its superiority over conventional FCM in capturing spatial relationships within the data. Our findings underscore the effectiveness of our approach in enhancing clustering outcomes by strategically incorporating neighbourhood information into the FCM framework for non-image data in Euclidean space.

Clustering, spatial FCM, nonimage data, Euclidian neighbour, FCMS.

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Kaushik Sarkar, Rajani K. Mudi, "Fuzzy Clustering Exploiting Neighbourhood Information for Non-image Data," International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.1-8, 2024.

Latency and jitter are two critical factors that can significantly impact performance of wireless networks. As reliance on wireless communication grows, it becomes imperative to address these issues to ensure seamless connectivity and enhance user experience. This abstract presents an overview of the challenges posed by latency and jitter in wireless networks and highlights potential solutions to mitigate these issues. Latency refers to the time delay experienced when data packets travel from the source to the destination across a network. Factors such as signal interference, network congestion, distance, and processing delays contribute to latency in wireless networks. High latency can disrupt real-time applications like video conferencing, online gaming, and voice calls, leading to compromised quality and user frustration. Jitter, on the other hand, represents the variation in packet arrival times at the receiving end. Inconsistencies in network traffic, packet routing, and transmission delays contribute to jitter. It can result in packet loss, out-of-order delivery, and disruptions in audio and video streams, particularly impacting time-sensitive applications such as streaming media and real-time communication. To address latency and jitter issues in wireless networks, several solutions can be implemented. Quality of Service (QoS) prioritization allows for the efficient management of network resources and prioritization of time-sensitive traffic, reducing latency and minimizing jitter for critical applications. Network optimization techniques, including strategic placement of access points, channel allocation, and signal strength optimization, can minimize interference and improve overall network performance. Bandwidth management techniques such as traffic shaping, prioritization, and bandwidth reservation help allocate network resources effectively, reducing congestion-induced latency and jitter. Implementing error correction mechanisms such as forward error correction (FEC) and retransmission techniques can compensate for packet loss and minimize the impact of jitter on data transmission. Optimizing signal strength and range through adjustments in transmit power, deploying additional access points, or utilizing signal repeaters extends the network`s coverage, reducing latency caused by distance and signal attenuation. By addressing these strategies, wireless networks can mitigate the effects of latency and jitter, resulting in improved performance and a better user experience. As wireless communication continues to play a vital role in our interconnected world, it becomes essential to focus on minimizing latency and jitter issues, ensuring reliable and efficient wireless connectivity.

Wireless Networks, Latency, Jitter, Network Optimization, Quality of Service, Retransmission, Scalability

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S. Mohanarangan, G. Shoba, K. Vaidegi, M. Hemamalini, "Joint Optimization Techniques to Mitigate Latency and Minimize the Jitter in Wireless Networks," International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.9-17, 2024.

To indicate the proper development of a child, there are certain baseline milestones. If a child is not reaching the milestones at the expected rate, it can indicate that there is an issue that needs to be addressed. By early intervention, the development of the child can be improved and the long-term impact of the developmental delays may be reduced. One such constraint of child development is Autism spectrum disorder. The ASD-affected children exhibit difficulties in communication, socialization and challenges in physical, social, and emotional development. This neurodevelopmental disorganization may exhibit an extensive range of effects and symptoms including challenges in communication, social interactions, and physical, social, and emotional behaviours. To identify ASD symptoms in a child, the range of ASD symptoms must be available as datasets to the researchers. The difficult phenomenon is that parents are not able to identify or detect early-age indications of ASD in their children. This proposed research work aims to detect the symptoms of ASD from parents’ dialogues. The dataset has collected data from many autism groups from social media and organizations for special children. To understand the sentiment of parents’ dialog there are two important and popular machine learning models, the Multinomial Naïve Bayes and the XGBoost. Naïve Bayes is based on a probabilistic machine learning model and XGBoost is an ensemble-oriented model. If new data comes from a new parent, the sentiment of that data is also predicted by these models. By using these two models, sentiment analysis can help to identify ASD symptoms. Based on the prepared data, the accuracy of these two models is 70% and 70% respectively.

Autism Spectrum Disorder, Machine Learning, ASD Detection, ML-based Framework, Traditional Machine Learning, Multinomial Naïve Bayes, XGBoost

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Prasenjit Mukherjee, Sourav Sadhukhan, Manish Godse, "Autism Spectrum Disorder Detection from Parents Dialogues Using Multinomial Naïve Bayes and XGBoost Models," International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.18-29, 2024.

Recommender systems play a vital role in providing pertinent content across diverse domains, such as entertainment, social networks, healthcare, education, travel, cuisine, and tourism. This review offers a thorough examination of cutting-edge recommender systems, as well as hybrid recommender systems. Hybrid models, combining different recommendation approaches, have gained prominence in enhancing system performance. The study classifies several models of hybridization and arranges the literature depending on the hybrid model and the applied machine learning methods in each study. Additionally, a systematic literature review examines the landscape of recommender systems over the last few years, emphasizing the quantitative aspects of research in this field. The review explores challenges, data mining techniques, recommendation strategies. It identifies common issues, such as addressing cold-start, accuracy, scalability and data sparsity, and highlights emerging challenges, including adapting to evolving user contexts and tastes.. Given the ongoing significance of hybrid recommenders, the review proposes exploring fresh possibilities such as utilizing parallel hybrid algorithms, and handling more extensive datasets, to address the evolving requirements of users.

Artificial Intelligence, Collaborative Filtering, Recommendation System, Hybrid Recommendation System, Data Mining.

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Yash Patil, Samidha Ashtikar, Sakshi Shirodkar, Krishna Dudhate, Shraddha V. Pandit, "Recommendation Systems in Online Retail: A Comprehensive Survey of AI Techniques," International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.30-36, 2024.

Phishing is a kind of cyberattack in which victims are tricked into divulging private information, including credit card numbers or passwords, by means of phoney emails or websites. Users may find it challenging to distinguish phishing websites from authentic websites due to their convincing appearance. This can lead to users entering their personal information on the phishing website, which can then be stolen by the attacker. An artificial intelligence technique called machine learning is used to train algorithms to find patterns in data. This can be used to create systems that automatically detect and alert users to potentially harmful websites, such as phishing website detection systems. The field of phishing website prediction currently faces some obstacles that require attention. The constant growth of phishing methods is one challenge. Artificial intelligence-based deep learning and machine learning techniques can identify phishing websites. Using machine learning techniques to predict phishing websites, we identify, monitor, and shield end users from monitoring based on phishing algorithms with respect to different publications. We present a machine learning method for phishing website identification in this research. Our method makes use of a number of characteristics, such as the URL structure, website content, and the existence of particular keywords or patterns, to discern between authentic and phishing websites. We test our method on a dataset of actual phishing websites, such as Google`s PhishCorp, Kaggle, and PhishTank, and we obtain a greater accuracy than the earlier studies on the detection of phishing websites. Our results show that machine learning can be an effective method for spotting phishing websites. With a better prototype and increased accuracy, our method is simple to use and can shield users from phishing assaults.

Phishing Websites, Machine Learning, A I, Accuracy, Precision, Error rate.

[1] F. Yahva et al., “Detection of Phising Websites using Machine Learning Approaches,” 2021 Int. Conf. Data Sci. Its Appl. ICoDSA 2021, pp.40–47, 2021, doi: 10.1109/ICODSA53588.2021.9617482.
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Ankit Prajapati, Chetan Agarwal, Pawan Meena, "Assessment of Phishing Websites Prediction using Machine Learning Approaches," International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.37-45, 2024.

Distributed Denial of Service (DDOS) attacks represent a tool wielded by hackers, cyber extortionist, and individuals involved in cyber terrorism. These malicious activities result in significant financial losses for the targeted victims. Although a myriad of scientific solutions are available, the frequency and intensity of DDOS attacks continue to escalate. In response to the evolving landscape of security threads, a novel network paradigm has emerged. Software Defined Networking (SDN) has garnered considerable attention from numerous researchers it addresses the specific requirement of modern data centres, drawing inspiration from its capabilities. Our study presents a comprehensive examination of current SDN-based solutions for detection and mitigating DDOS attacks. Building upon our findings, we propose an innovative framework designed for the detection and mitigation of DDOS attacks on a large scale network. The primary contribution of this paper is twofold. Firstly, we furnish an in-depth survey of SDN based DDOS attack detection and mitigation mechanisms. Secondly, we offer a detailed comparison of SDN- based controllers.

DDOS, SDN, cyber terrorism, financial losses, Controllers.

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R. Karhikeyani, E. Karthikeyan, "A Survey on Software defined Networking and different types of controllers," International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.46-52, 2024.