9.30 am -12.30 pm

Thesis Title :

ENHANCING CONSUMERS’ INTENTION TOWARD RECYCLING BEHAVIOR: AN APPROACH OF PRO-ENVIRONMENTAL CONSUMER BEHAVIOR (PECB)

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9.30 am -11.30 am

Thesis Title :

EARLY WARNING FRAMEWORK FOR IDENTIFYING AT-RISK UNIVERSITY STUDENTS UTILIZING WINDOWED-TEMPORAL MACHINE LEARNING TECHNIQUES

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2.30 pm -5.30 pm

Thesis Title :

DEVELOPMENT OF DISTRIBUTED MAGNETIC FIELD OPTICAL FIBRE SENSOR BASED ON MAGNETOSTRICTION METHOD

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2.30 pm -4.30 pm

Thesis Title :

HYBRID COMPRESSION-ENCRYPTION MODELS ON BIOMEDICAL SIGNALS FOR TELEMEDICINE AND STORAGE APPLICATIONS

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2.30 pm -4.30 pm

Thesis Title :

INTEGRATION OF AI-DRIVEN SYSTEMS WITH OPEN CHARGE POINT PROTOCOL FOR ENHANCED ELECTRIC VEHICLE CHARGING MANAGEMENT

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9.30 am -11.30 am

Thesis Title :

INFLUENCE OF MINDFULNESS AND SELF-COMPASSION PRACTICE FOR WELL-BEING AMONG UNIVERSITY STUDENTS IN MALAYSIA TO ENHANCE THE  LEVEL OF RESILIENCE

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2.30 pm -4.30 pm

Thesis Title :

BREAST CANCER NUCLEI SEGMENTATION AND CLASSIFICATION IN HISTOPATHOLOGY IMAGES USING DEEP LEARNING

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10.30 am -12.30 pm

Thesis Title :

COST-EFFECTIVE MACHINE LEARNING MODELS FOR THE EARLY PREDICTION OF COGNITIVE IMPAIRMENT USING ACCESSIBLE DATA

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2.30 pm -4.30 pm

Thesis Title :

FRAMEWORK FOR CASSAVA PLANT CONDITION ASSESSMENT BASED ON DISEASE LEAF DETECTION

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9.30 am -12.30 pm

Thesis Title :

STUDENTS PERFORMANCE EVALUATIONS IN LEARNING MATHEMATICS THROUGH CONVERSATIONAL-BASED AGENT

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2.30 pm -4.30 pm

Thesis Title :

DRIVER DROWSINESS DETECTION USING PHYSIOLOGICAL SIGNALS AND DEEP TEMPORAL MODELS

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9.30 am -11.30 am

Thesis Title :

HYPERSPECTRAL IMAGE CLASSIFICATION FOR GANODERMA DISEASE EARLY DETECTION THROUGH DEEP NEURAL NETWORK

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2.30 pm -5.30 pm

Thesis Title :

DIGITAL IMAGE BLIND FORENSICS: COPY-MOVE FORGERY DETECTION

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2.30 pm -4.30 pm

Thesis Title :

SPATIAL ANALYSIS AND RISK MODELLING OF DISEASE OUTBREAK USING SATELLITE IMAGERY

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9.30 am -12.30 pm

Thesis Title :

ROGUE ACCESS POINT IDENTIFICATION VIA BEACON ANOMALY DETECTION

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2.30 pm -5.30 pm

Thesis Title :

NOISE ANALYSIS AND DENOISING IN SCANNING ELECTRON MICROSCOPY IMAGES USING DEEP LEARNING

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