IMPLEMENTATION OF LOW POWER MEMRISTOR CONTENT ADDRESSABLE MEMORY USING FINFET
The research environment is promptly looking at the extensive development of memristor devices in industrial applications. Future technology is eagerly waiting for the upcoming developments in memristor-based devices. Memristor regulates the current flow in devices and the amount of previously flowed charges, which are stored as memory in applicati...
By Ancy Joy, Jinsa Kuruvilla
THE SURVEY OF CLUSTER BASED DATA COLLECTION PROCESS FOR IOT ENABLED WIRELESS SENSOR NETWORK USING SEVERAL OPTIMIZATION TECHNIQUE
Offers a detailed analysis of optimization algorithms and routing protocols are created to overcome the issues on energy efficiency with the Internet of Things (IoT) Enabled Wireless Sensor Networks (WSNs). The study analyses nature-based metaheuristic methods such as the Genetic Algorithms, Particle Swarm Optimization, Firefly Optimization, Gray W...
By R. Abirami, K. Sathishkumar, Liu Guanzhou, M. Ramalingam, Wasim Ahmad, Ali Bostani
CROSS-PLATFORM HATE SPEECH DETECTION BY TARGET CATEGORY: EVALUATING TRADITIONAL AND TRANSFORMER MODELS ENHANCED WITH SMOTE
Societal and technological challenges are significant when it comes to cyberbullying, which is an ubiquitous issue in the social media space including Twitter, Facebook, YouTube, and Instagram. This paper will focus on the identification of hate speech that targets particular individuals, especially in the context of the data in Hindi language. It ...
By Rachna Narula, Poonam Chaudhary
BIO-INSPIRED ADAPTIVE ANOMALY DETECTION IN IOT USING ARTIFICIAL IMMUNE SYSTEMS AND DYNAMIC DETECTOR SELECTION
The rapid growth of the Internet of Things (IoT) brings new options to innovative healthcare, transportation, and industrial systems. However, this expansion also increases cyber threats to these infrastructures. Standard anomaly detection systems use fixed machine learning models. Such models require frequent retraining and are not very sensitive ...
By Ashraf Thaker Mahmood, Qais Rashid Ibrahim
HYBRID HYDROGEN ELECTRIC POWERTRAIN OPTIMIZATION FOR CONNECTED AUTONOMOUS VEHICLES IN URBAN TRAFFIC
The transition to low-carbon urban transport has catalyzed interest in hybrid hydrogen-electric powertrains for connected autonomous vehicles (CAVs), which offer zero tailpipe emissions, long driving range, and the potential to refuel quickly. The paper proposes a system-level optimization model for hybrid hydrogen-electric powertrains in urban tra...
By Anjali Goswami, Anjali Krushna Kadao
METARFM: A META-LEARNING FRAMEWORK FOR THE ADAPTIVE SELECTION OF RFM MODEL ARIANTS IN CUSTOMER SEGMENTATION
The Recency-Frequency-Monetary (RFM) model is a widely used method for customer segmentation, but its effectiveness depends on selecting the appropriate variant (e.g., weighted or entropy-based) for a given dataset. This selection process is typically manual and task-specific, leading to inconsistent results and limited generalizability. To address...
By F. Mary Magdalene Jane, V. Pream Sudha, S. Saranya, P. Usha, V. Santhana Lakshmi, S.R. Kalaiselvi
NEUROMORPHIC-INSPIRED HYBRID COGNITIVE MODEL FOR SELF-OPTIMIZING RESOURCE MANAGEMENT IN 6G EDGE NETWORKS
Introduction: The 6G world requires connected intelligence, but there is a crucial paradox between the standards of Large Language Model (LLM) and edge constraints. The premium devices have up to 6-12GB of DRAM, whereas the typical 175B models need 350GB of storage, which is 30 times that of the premium version. Literature Survey: It has been...
By Reji K Kollinal, Mariya T Cheeran
AGILE PROJECT MANAGEMENT STRATEGIES FOR OPTIMIZING CROSS-FUNCTIONAL OPERATIONS AND MARKET ADAPTATION IN HIGH-TECH SUPPLY CHAINS
An environment that is characterised by a high rate of technological change, short product life cycle, and fluctuating market demand demands a high level of technology, and therefore, the conventional project management approaches may no longer be effective. This paper analyzes how agile project management strategies can be used to support cross-fu...
By M.N. Prabadevi, P.A. Mary Auxilia, B. Jeyaprabha, N. Zeenath Zarina, M. Menaka
IMPROVING MICRO-EXPRESSION RECOGNITION WITH AN ENHANCED DESCRIPTOR COMBINING GW-LBP, TGMH, AND WT
Micro-expressions (MEs) are involuntary facial expressions, short-lived (usually between 1/5 and 1/25 seconds), and important in the application of security, psychological tests, and forensics. The MEs are however difficult to identify because it occur quickly and also involve little movement of the muscles. The paper presents an Enhanced Micro-Exp...
By P. Surekha, P. Vidya Sagar, G. Ramesh
BIG DATA PROCESSING AND CORRELATION ANALYSIS OF ELECTRIC POWER MARKETING BASED ON IMPROVED APRIORI ALGORITHM AND RDD MODEL
To solve the problems of traditional Apriori algorithm in power marketing big data processing, such as candidate item set redundancy, low single-machine computing efficiency, and difficulty in adapting to multi-dimensional time series data, this study proposes an improved Apriori algorithm that integrates Resilient Distributed Dataset (RDD) distrib...
By Fan Pan, Lingen Zhou, Lu Gan, Wei Kang, Xiaolei Li