FEATURE SELECTION METHOD USING HYBRID SWARM WITH IMPROVED FUZZY C-MEANS CLUSTERING IN DATA MINING FOR DISEASE DETECTION
A crucial method for reducing the dimensionality issue in DM (Data Mining) tasks is FS (Feature Selection). Conventional techniques for FS do not scale well in vast spaces. The HPSO-IKM approach has a rather long processing time, so future studies will keep enhancing the technique's stages to reduce the duration of detection. PSO's poor local searc...
By M. Birundha Rani, Dr.A. Subramani
A ROBUST MACHINE LEARNING-BASED ENSEMBLE LEARNING FRAMEWORK FOR HATE SPEECH DETECTION IN LOW-RESOURCE SOCIAL MEDIA TEXT
The low-resource social media text i.e., Urdu tweets containing hate speech are identified with the help of a machine learning-based ensemble approach. The dataset used for this study consisted of 8,800 tweets and half of them were labeled as Hateful and the other half as No-Hate. In preprocessing, we took into account the features of Urdu normaliz...
By Husnain Saleem, Muhammad Javed, Kiran Hanif, Asad Ullah, Muhammad Usman Ghani, Muhammad Waqas, Muhammad Ali Khan, Sheraz Ali Hassan
EXAMINING THE INTERSECTION OF REALISM AND EXPERIMENTAL NARRATIVES IN CONTEMPORARY LITERATURE
The merging of the traditions and novelty in the later works of literature is the convergence of realism and experimental narratives. Where realism has its goals and aims on the true representations of society, the experimental narratives disrupt the norms by their disjointed structures and unreliable narrators. This paper will explore how modern w...
By Zulfiya Pardaeva, Oleg Kim, Dilorom Khodjimetova, Yuliya Isaeva, Umid Kholnazarov, Sevara Berdimurotova, Mohinur Farmonova, Zokhida Amirova
ENHANCING IP COMMERCIALIZATION PERFORMANCE IN SOCIAL SCIENCE ACADEMICS AND THE ROLE OF ENTREPRENEURIAL ORIENTATION, UNIVERSITY SUPPORT, AND SELF-EFFICACY
This research aims to propose and empirically examine a framework that explains how entrepreneurial orientation elements contribute to intellectual property commercialization performance among social science academicians in Malaysian public universities. Specifically, the framework explores the mediating role of university support and the moderatin...
By Khairil Azfar Bin Ahmad Zubir, Muhamad Ali Imran Bin Kamarudin
INTEGRATION OF IOT IN SMART BUILDING SYSTEMS FOR SUSTAINABLE URBAN DEVELOPMENT
The rapid growth of urban populations has exacerbated energy waste, resource inefficiency, and environmental degradation in contemporary cities, necessitating crucial, smart building solutions. This paper examines the incorporation of the Internet of Things (IoT) in intelligent buildings to facilitate sustainable urban development. The study helps ...
By Dawakit Lepcha, Kanchan Thakur
DESIGN AND IMPLEMENTATION OF AN AI-BASED MEDICAL ANALYTICS FRAMEWORK EMPLOYING DEEP NEURAL NETWORKS AND ADVANCED MACHINE LEARNING MODELS FOR PRECISION HEALTHCARE
The digitalization of healthcare is fast, bringing about abundant and diverse medical data with novel opportunities to bring precise healthcare with the help of artificial intelligence (AI) associated analytics. Traditional methods of data analysis in medical fields frequently do not describe nonlinear and complex relationships in multimodal clinic...
By B. Senthilkumaran, Gajraj Singh, Ali Bostani, S. Krithika, Zarina Khalbayeva, R. Ushasree, P. Nanthini
DATA-DRIVEN SUPPLY CHAIN AND FINANCIAL MANAGEMENT FRAMEWORK FOR RISK OPTIMIZATION IN HIGH-TECHNOLOGY MANUFACTURING INDUSTRIES
In the dynamic environment of the high-technology production, the supply chain and financial risks management have become more important to maintain the continuity of the operations and profitability. Although a large amount of data is available, most industries continue to struggle to use this data to optimize all risks holistically. In this paper...
By Priya Sethuraman, M. Kalaivani, K. Latha, B. Kiruthiga
MEDIAN ATTRIBUTE HYBRID CLUSTERED MODEL USING PARTICLE SWARM OPTIMIZATION FOR NETWORK INTRUSION DETECTION
The rapid growth of cloud-based and large-scale network infrastructures has increased the complexity and frequency of cyber-attacks, demanding efficient and scalable intrusion detection systems (IDS). This paper will also attempt to create a better Network Intrusion Detection System (NIDS) by incorporating a Hybrid Median Attribute Clustering model...
By Rajasekhar Kaseebhotla, Raghava Rao, Mallikarjuna Rao
DEEP REINFORCEMENT LEARNING CONTROL OF SWARM UAVS FOR POST DISASTER CIVIL INFRASTRUCTURE ASSESSMENT
Rapid and accurate assessment of civic infrastructure following a natural or artificial disaster is essential to planning emergency response and recovery. This paper introduces a control system based on deep reinforcement learning (DRL) to coordinate unmanned aerial vehicle (UAV) swarms and methodically approach the post-disaster infrastructure ins...
By Moti Ranjan Tandi, Archana Mishra
PERFORMANCE ANALYSIS OF DOWN SYNDROME DETECTION SYSTEM USING ULTRA SOUND FETUS IMAGES AND DEEP LEARNING CLASSIFICATION METHODS
Down Syndrome (DS) is a genetic disorder due to a partial copy of the 21 st chromosome in the fetus, usually detected by invasive techniques like Amniocentesis and Chorionic Villus Sampling (CVS) which have chances of miscarriage. This paper suggests a completely automated and computer-aided method of non-invasive detection of DS by ultrasound fetu...
By V. Gokulakrishnan, S. Selvakumar