Ketan Kotecha

• Doctor of Philosophy
• Head of Faculty at Symbiosis International University

ASD is a mental developmental disorder that significantly impacts the behavioural and communicational abilities of the child. ASD is affecting the world hard, and its global presence continuously increases. One of the reasons for this trend may be a pandemic, which increases screen time for children and decreases communication with peers or family....

The freshness of fruits, especially papayas, is essential for consumer satisfaction and marketability. This article introduces PapayaFreshNet, an innovative deep learning network designed for the automatic categorization of papaya freshness through picture analysis. The architecture integrates the advantages of Convolutional Neural Networks (CNNs)...

Reinforcement learning, characterized by trial-and-error learning and delayed rewards, is central to decision-making processes. Its core component, the reward function, is traditionally handcrafted, but designing these functions is often challenging or impossible in real-world scenarios. Inverse reinforcement learning (IRL) addresses this issue by...

This study presents a novel approach using the Demographic Bias Mitigation Framework (DBMF), which leverages Domain Adaptation (DA) to mitigate demographic biases in affective computing AI systems. Biases in technologies like sentiment analysis (SA) and facial emotion recognition (FER) can result in adverse societal impacts and diminished trust. Un...

Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom trials. Differentiating truth from lies is inherently challenging due to many complex, diversified behavioural, physiological and cognitive aspects. Traditional lie detector tests (polygraphs) have been widely used but remain controversial due to sci...

With advancements in software development and artificial intelligence, defect prediction has gradually become an essential component of the software development lifecycle. Historically, defect prediction has been considered a multiclass classification problem because defect classes are mutually exclusive. However, software defects can belong to mul...

Purpose-This study aims to explore and understand the literature on digital transformation (DT) research that will lead to developing a conceptual and thematic structure of DT management. Design/methodology/approach-The research approach employed a hybrid approach of bibliometric analysis and a structured review of DT management research studies fr...

Reinforcement Learning (RL) is a biologically inspired, autonomous machine learning method. RL algorithms can help generate optimal predictive maintenance (PdM) policies for complex industrial systems. However, these algorithms are extremely sensitive to hyperparameter tuning and network architecture, and this is where automated RL frameworks (Auto...

Growing threats in public spaces have forced people to question personal security, making technology more relevant, especially in speech recognition. This paper proposes a security safety system by considering keyword and negative emotion detection to solve this problem. It detects the wake-up word "ON" whenever it is spoken with negative emotion....

The task of emotion detection in online social communication has been explored extensively. However, these studies solely focus on textual cues. Nowadays, emojis have become increasingly popular, serving as a visual means to express emotions and ideas succinctly. These emojis can be used supportively or contrastively, even sarcastically, adding com...

Accurate and precise identification of cholelithiasis is essential for saving the lives of millions of people worldwide. Although several computer-aided cholelithiasis diagnosis approaches have been introduced in the literature, their use is limited because Convolutional Neural Network (CNN) models are black box in nature. Therefore, a novel approa...

Academic and research papers serve as valuable platforms for disseminating expertise and discoveries to diverse audiences. The growing volume of academic papers, with nearly 7 million new publications annually, presents a formidable challenge for students and researchers alike. Consequently, the development of research paper summarization tools has...

The applications of drones for smart farming are well accepted nowadays. It also results in huge fiscal losses to the agricultural economy. In conventional agriculture, resources are wasted due to the constant and uniform use of pesticides, fertilizers, and pharmaceuticals. Nevertheless, within the existing literature, no comprehensive approach to...

Stemming plays a crucial role in natural language processing and information retrieval. It is challenging for the Gujarati language due to the complex morphology of several stemming algorithms for the Gujarati language that have been developed using rule-based, dictionary-based, or hybrid approaches. However, they are computationally expensive, pro...

Numerous computer-aided leukemia detection methods have been introduced to overcome the limitations of clinical diagnosis procedures. The precision of computer-aided leukemia detection highly depends on the accurate segmentation of white blood cells (WBCs) from the stained whole slide image (WSI). This paper proposes WBC segmentation from WSI using...

Bio-inspired optimization algorithms use natural processes and biological phenomena as a basis for solving difficult optimization issues. This article discusses state-of-the-art techniques, applications, and implementations of eleven well-known bio-inspired optimization algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), A...

Nature-inspired optimization approaches play a vital role in fostering smart cities by adopting natural system efficiency. These approaches, which are founded on phenomena in biology, ecology, and physical science, optimize resource use, energy and transportation systems. They offer new possibilities for intelligent cities to mimic naturally occurr...

In autonomous driving systems, the semantic segmentation task involves scene partition into numerous expressive portions by classifying and labelling every image pixel for semantics. The algorithm used for semantic segmentation has a vital role in autonomous driving architecture. This paper's main contribution is optimising the semantic segmentatio...

Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of qu...

Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models di...

Object discovery, the task of separating objects from the background without human annotations, continues to be an unsolved challenge in the field of computer vision. Existing methods face difficulties due to object-background ambiguity and slot drift, as they rely on clustering based on only low-level features. In this work, we introduce DiscoverN...

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders that affect the quality of human life of millions of people throughout the world. The probability of getting affected by this disease increases with age, and it is common among the elderly population. Early detection can help in initiating medications at an earlier stage...

Household electricity consumption (HEC) is changing over time, depends on multiple factors, and leads to effects on the prediction accuracy of the model. The objective of this work is to propose a novel methodology for improving HEC prediction accuracy. This study uses two original datasets, namely questionnaire survey (QS) and monthly consumption...

This paper describes a method that can perform robust detection and classification in out-of-distribution rotated images in the medical domain. In real-world medical imaging tools, noise due to the rotation of the body part is frequently observed. This noise reduces the accuracy of AI-based classification and prediction models. Hence, it is importa...

Undesirable vibrations generated during milling significantly intensify milling tool wear and reduces tool life. Hence it is imperative to employ the effective vibration control strategy during milling to optimize the overall effectiveness of the process. Thus, the present study incorporates the magneto-rheological fluid damping for vibration mitig...

Wildfires rank among the world’s most devastating and expensive natural disasters, destroying vast forest resources and endangering lives. Traditional firefighting methods, reliant on ground crew inspections, have notable limitations and pose significant risks to firefighters. Consequently, drone-based aerial imaging technologies have emerged as a...

Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a sel...

Recently, Artificial Intelligence (AI) has seen significant progress, especially in Natural Language Processing (NLP) and Conversational AI, making response generation more efficient. This advancement, combined with increased availability of conversational data, has greatly improved conversational bots, thus enhancing their effectiveness and scope....

Defect detection in pharmaceutical blister packages is the most challenging task to get an accurate result in detecting defects that arise in tablets while manufacturing. Conventional defect detection methods include human intervention to check the quality of tablets within the blister packages, which is inefficient, time-consuming, and increases l...

In this study, the research presents the Federated Learning Enhanced Multi-Layer Perceptron (Fed-MLP) Long Short-Term Memory that is suggested by the research. The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learni...

In this study, the research presents the Federated Learning Enhanced Multi-Layer Perceptron (Fed-MLP) Long Short-Term Memory that is suggested by the research. The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learni...

This paper proposes a knowledge-based decision-making system for energy bill assessment and competitive energy consumption analysis for energy savings. As humans have a tendency toward comparison between peers and self-groups, the same concept of competitive behavior is utilized to design knowledge-based decision-making systems. A total of 225 hous...

This study examines the intricate interplay between the digital environment and the evolving communication dynamics of Generation Z, specifically focusing on the impact of social media on familial bonds. The research objective is to explore the ways in which Generation Z's social media consumption patterns shape their relationships and lives, provi...

This study examines the intricate interplay between the digital environment and the evolving communication dynamics of Generation Z, specifically focusing on the impact of social media on familial bonds. The research objective is to explore the ways in which Generation Z's social media consumption patterns shape their relationships and lives, provi...

Intrusion detection identifies malicious activity in a computer system or network. It is a critical component of any information security system, as it can help to protect against unauthorised access, data theft, and other forms of cyberattacks. Traditional intrusion detection techniques have limitations, such as signature-based and anomaly-based d...

In the current industrial landscape, a significant number of sectors are grappling with the challenges posed by unstructured data, which incurs financial losses amounting to millions annually. If harnessed effectively, this data has the potential to substantially boost operational efficiency. Traditional methods for extracting information have thei...

Understanding the emotions and sentiments from conversations has relevance in many application areas. Specifically, conversational agents, question-answering systems, or areas where natural language inference is used. Therefore, techniques to detect emotions from conversations have become the need of the moment. The convolutional network and recurr...

As an evolving growth of digitalization in the global World, there is an urgent need to keep an eye on the protection of our secret data. These secret data can be protected using various cryptographic techniques. Due to current rapid development in the field of quantum computing and quantum algorithms, many traditional cryptographic algorithms are...

This research paper delves into the utilization of facial expressions as social cues in online platforms, emphasizing the increasing relevance of online learning and remote work. By employing a sophisticated fusion of data, image, and feature-level analysis, complemented by multi-classifier systems, this study capitalizes on the strengths of Siames...

Engineered Science F o r R e v i e w O n l y Proposed GCN-based approach leveraging PMI and graph structure for health misinformation detection, outperforming traditional ML models across multiple datasets. Abstract: In the age of rapid information dissemination through social media, combating health misinformation is a critical challenge, particul...

This paper introduces a novel end-to-end framework for story generation utilising deep neural networks. Generating text belongs to a vital part of Natural Language Processing (henceforth, NLP), and different areas of its applications, including creative writing, entertainment, and education, made it a subject of this research interest. Traditional...

A rolling bearing is a crucial element within rotating machinery, and its smooth operation profoundly influences the overall well-being of the equipment. Consequently, analyzing its operational condition is crucial to prevent production losses or, in extreme cases, potential fatalities due to catastrophic failures. Accurate estimates of the Remaini...

Digitization created a demand for highly efficient handwritten document recognition systems. A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an essential element of handwritten documents. Accurate recognition of handwritten digits is vital for effective communication and data analysis. Various researchers have at...

Accurate and timely crack localization is crucial for road safety and maintenance, but image processing and hand-crafted feature engineering methods, often fail to distinguish cracks from background noise under diverse lighting and surface conditions. This paper proposes a framework utilizing contextual U-Net deep learning model to automatically lo...

Autism spectrum disorder (ASD) is a complex developmental issue that affects the behavior and communication abilities of children. It is extremely needed to perceive it at an early age. The research article focuses on attentiveness by considering eye positioning as a key feature and its implementation is completed in two phases. In the first phase,...

Industry 4.0 requires digital twins (DTs), a potential technology in the transition. By offering current operational statistics visualizations of physical assets, assisting in decision-making, and mitigating possible hazards in electronic manufacturing environments, DTs are essential to the improvement of distributed consumer electronics. DTs must...

Attention mechanism has recently gained immense importance in the natural language processing (NLP) world. This technique highlights parts of the input text that the NLP task (such as translation) must pay “attention” to. Inspired by this, some researchers have recently applied the NLP domain, deep-learning based, attention mechanism techniques to...

Изучение цифрового юмора и культуры мемов как комплексного явления среди носителей цифровых технологий в сочетании с дефицитом научного внимания и исследований на эту тему в Иране становится все более актуальным. Данная статья посвящена сложному взаимодействию между мемами, культурой и технологиями. Анализируя неотъемлемую роль юмора, в частности к...

To address the limitations of 5G, 6G wireless networks are envisaged to provide sub-millisecond latency, ultra-high connection density, extremely high data rates, better coverage, reliability, availability, etc., for cloud computing, the Internet-of-Everything, and cyber-physical systems. However, avoiding several trust-related issues in the design...

The integration of multiple technical, economic, environmental, and social criteria establishes Multi-Criteria Decision Analysis (MCDA) as a dependable decision-making tool in the context of interdisciplinary research. This study employs a literature-based methodology to illustrate how MCDA, particularly utilizing the Analytical Hierarchy Process (...

In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional mach...

INTRODUCTION: Segregating hepatic tumors from the liver in computed tomography (CT) scans is vital in hepatic surgery planning. Extracting liver tumors in CT images is complex due to the low contrast between the malignant and healthy tissues and the hazy boundaries in CT images. Moreover, manually detecting hepatic tumors from CT images is complica...

Diabetes, a chronic metabolic disease with a rising global prevalence, significantly impacts individuals’ health. Diabetes increases a person’s risk of developing various diseases, including heart disease, stroke, vision problems, nerve damage, etc. Early detection and proactive care of diabetes can lessen its impact and improve patient outcomes. U...

This study explores the potential and application of the newly proposed Forward-Forward algorithm (FFA). The primary aim of this study is to analyze the results achieved from the proposed algorithm and compare it with the existing algorithms. What we are trying to achieve here is to know the extent to which FFA can be effectively deployed in any ne...

This article introduces a method for classifying diabetes based on machine learning (ML) methods. In recent years, significant focus have been put onto increasing disease classification performance through the use of ML approaches. This paper outlines the use of five interpretable ML algorithms: Bagging classifier, Random Forest, AdaBoost, Multilay...

The primary objective of this research paper is to develop an efficient method for the early identification of heart failure. Two classification techniques—Logistic Regression (LR) and Naive Bayes (NB)—were used in a series of experiments utilizing the heart failure dataset from the UCI repository. The authors selected accuracy as the performance m...

The classification of Electroencephalogram (EEG) signals into distinct frequency bands is a critical task in understanding brain function and diagnosing neurological disorders. The information obtained from frequency-specific classification has multiple applications, such as frequency-based wheelchair control, frequency-based 36-stroke brain operat...