ANALYZING AND PRIORITIZING HEALTHCARE SERVICE PERFORMANCE IN HOSPITALS USING SERQUAL MODEL
Health care is essential for both public welfare and national growth. Nevertheless, the number of hospitals has increased; patients still face many problems highlighting the need for better healthcare services. The Present study aims to analyze the quality of healthcare services in view of patients’ fulfillment and identify their requirements...
By Abdul Z. Hameed, R. Balamurugan, Ali Rizwan, Muhammad Atif Shahzad
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
A COMPARATIVE STRATIFICATION OF FISH SPECIES USING TRANSFER LEARNING ON PRE-TRAINED DEEP LEARNING NETWORKS JUXTAPOSED WITH SHUFFLERES – A HYBRID DEEP NETWORK CLASSIFIER
The marine ecoculture is an evolving realm that necessitates thorough scrutiny of the diverse species it comprises, along with the explicit identification of the species classes that form, to be crucial for aquaculture and the ecological conservation of fish diversity. The stratification through image classification is a well-studied area of resear...
By R.P. Selvam, R. Devi
PRECISION STOCK MARKET TREND ANALYSIS WITH HYBRID SMOOTH SVM AND WEIGHED VULTURE OPTIMIZATION
Accurate prediction of stock market trends remains a challenging task due to high volatility, non-linearity, and the dynamic nature of financial time series data. Conventional statistical and machine learning typically do not provide consistent performance due to the fixed hyperparameter settings and the inability to adapt to a shifting market situ...
By N. Subalakshmi, M. Jeyakarthic, V. Mohanaselvam
ENTREPRENEURIAL MARKETING AND INNOVATION CAPABILITIES IN HIGH TECHNOLOGY STARTUPS IN EMERGING ECONOMIES
Emerging economies have a dynamic and challenging business environment, which requires entrepreneurial marketing and innovation capabilities of the high-tech startups. Such startups are usually faced with issues of lack of resources, regulation and lack of proper infrastructure. In this regard, it is important to combine entrepreneurial marketing p...
By Jainish Roy, Rajesh Sehgal
LEVERAGING NANOMATERIALS IN CHEMICAL ENGINEERING TO OPTIMIZE EFFICIENCY AND SUSTAINABILITY ACROSS MODERN INDUSTRIAL PROCESSES
Nanomaterials have distinct physicochemical characteristics of high surface area, controllable surface chemistry, and size-dependent reactivity, which can be used to fine-tune industrial chemical reactions to maximize efficiency and sustainability. The paper examines the incorporation of the following nanomaterials, namely carbon nanotubes, graphen...
By Sandeep Soni, Rajvir Saini
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