Digital Transformation in Maritime Supply Chains: A Systematic Review of DIS Platforms

Digital transformation plays a key role in improving information sharing and information processing in supply chains. Specifically, maritime supply chains require numerous data and document exchanges and can significantly benefit from digital information sharing (DIS). This notable potential has attracted attention and has resulted in a growing number of studies on blockchain platforms, cloud-based platforms, and other digital technology platforms. However, DIS adoption and execution is a complex process as it depends on various success factors and barriers and affects numerous capabilities and performance outcomes. Moreover, various information systems and management theories can be utilised to underpin these relationships. Our study aims to conduct a systematic literature review that uncovers dynamic capabilities, barriers, enablers and outcomes of DIS with blockchain and cloud-based platforms, illustrates the relationship between them, and discloses methods and theories applied in supply chains. We discuss different use cases of blockchain and cloud-based platforms for DIS in various business functions in supply chains. Particularly, we reveal six DIS-powered capabilities, five performance outcomes improved by the DIS, eight main barriers, and nine enablers of DIS implementation. The lack of theoretical underpinning and causal empirical studies is identified as an important gap in the literature. This study also presents precise future research directions that can help address these gap

Harmonizing Multi-Omics for Enhanced Machine Learning

The proliferation of high-throughput technologies has yielded an abundance of omics data, spanning diverse biological layers such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Machine learning algorithms have harnessed this data deluge, yielding diagnostic and classification biomarkers. However, prevailing biomarkers predominantly rely on single omic measurements, overlooking the potential insights from multi-omics experiments that encapsulate the entirety of biological complexity. To fully exploit the wealth of information embedded in different omics layers, effective multi-omics data integration strategies become imperative. This minireview categorizes recent integration methods/frameworks into five strategies: early, mixed, intermediate, late, and hierarchical. Our focus is on delineating challenges and exploring existing multi-omics integration strategies, with a keen emphasis on their application in machine learnin

Vehicle Insurance Fraud Detection Using Machine Learning

There are thousands of companies in the insurance industry globally, and collect premiums totaling more than $1 trillioneachyear.Insurance fraud occurs when a person or organisation submits a fake insurance claim in an effort to collect money or benefitsto which they are not legally entitled. An insurance fraud is thought to have a total financial impact of over$40 billion. Deterringinsurance fraud is thus a difficult issue for the insurance sector. The established method for detecting fraud is focused on creat-ing heuristics around fraud indicators. The most prevalent form of  insurance fraud is auto fraud, which is accomplished by filing false accident claims. This essay focuses on finding auto-vehicle fraud

Detecting Insurance Fraud: A Study on Field Fires with Computer Vision and IoT

The article suggests an automated system for overseeing the fraud detection process related to insurance claims for field fires in agriculture. This innovative solution combines computer vision, deep learning, and the Internet of Things (IoT) to leverage the strengths of each technology. As far as our knowledge extends, such an integration of these technologies has not been previously employed for analyzing insurance fraud in agriculture. The model actively monitors input from IoT devices equipped with infrared and temperature sensors. When these sensor values surpass predefined thresholds, the IoT device captures images of the field. These images are then processed by a fire detection model trained with various classifiers, allowing for performance comparisons. The reported results indicate an impressive accuracy of 97%, with potential for further improvement through a refined dataset specifically tailored for fraud detection

Fake News Detection using Machine Learning

Fake News has become one of the major problem in the society. It is due to its ability tochange opinions and cause lot of social and national damage with destructive impacts. Sometimes it gets very difficult to know if the news is genuine or fake. Therefore it is veryimportant to detect if the news is fake or not. The proposed project uses NLP techniques andMachine Learning to create models which can help to detect fake news. The datasets in thecomma separated values format, different attributes like the title and text of the newsheadline/article were used to perform Fake News Detection. The results show that theproposed solution performs well in terms of providing an output with good accuracy, precision, recall, F1 score. So the proposed project uses datasets that are trained using countvectorizer method for the detection of fake news and its accuracy will be tested usingmachine

CYBER CRIME : A Review

Cybercrime, a broad category of illicit activities conducted via computer networks and the internet, spans a spectrum of malicious behaviours. These include disrupting network operations, stealing sensitive data, hacking into bank systems for financial gain, perpetrating various forms of fraud, distributing child pornography, trafficking in illicit materials, stealing intellectual property, committing identity theft, and violating privacy rights. The repercussions of cybercrime ripple far beyond mere financial losses. They manifest in economic disruption, as cyberattacks can cripple businesses, disrupt supply chains, and destabilize financial markets. Moreover, victims of cybercrime often endure psychological distress, experiencing anxiety, fear, and a sense of violation due to the invasion of their privacy and loss of control over personal information. Collaboration and partnerships between governments, law enforcement agencies, industry stakeholders, and civil society organizations are crucial. By sharing intelligence, resources, and best practices, these entities can enhance their collective ability to combat cyber threats effectively.cybercrime presents a multifaceted challenge that demands a comprehensive response. By implementing a combination of cybersecurity measures, legal frameworks, educational initiatives, and collaborative efforts, stakeholders can work together to mitigate risks, safeguard individuals and businesses, and uphold security and trust in the digital realm

Heart Attack Analysis Using Machine Learning

This study explores the intersection of artificial intelligence, specifically machine learning, with healthcare to address the complex challenges in the analysis of heart attacks. Recognizing the limitations of traditional diagnostic methods for cardiovascular diseases, the research emphasizes the potential of machine learning algorithms to provide more accurate and nuanced insights. The methodology involves the integration of machine learning into the diagnostic landscape, aiming to bridge gaps in understanding and enhance predictive modeling. The specialized software proposed seeks to leverage advanced algorithms for processing complex datasets, offering healthcare professionals actionable insights for early diagnosis. Ethical considerations and regulatory compliance are paramount in the development of such software, ensuring the confidentiality and trustworthiness of healthcare data. Ultimately, this study envisions a shift from reactive to proactive healthcare strategies, revolutionizing how heart attacks are diagnosed and prevented

Multidimensional CNN and LSTM for Predicting Epilepsy Seizure Activities

Epilepsy is a chronic neurological disease caused by sudden abnormal brain discharges, leading to temporary brain dysfunction. It can manifest in various ways, including paroxysmal movement, sensory, autonomic nerve, awareness, and mental abnormalities. It is now the second largest neurological disorder worldwide, affecting around 70 million people and increasing by approximately 2 million new cases each year. While about 70% of epilepsy patients can control their seizures with regular antiepileptic drugs, surgery, or nerve stimulation treatments, the remaining 30% suffer from intractable epilepsy without effective treatment, causing significant burden and potential danger to their lives. Early prediction and treatment are crucial to prevent harm to patients. Electroencephalogram (EEG) is a valuable tool for diagnosing epilepsy as it records the brain's electrical activity. EEG can be divided into scalp and intracranial types, and doctors typically analyze EEG signals of epileptic patients into four periods

Impact of IoT and Cloud Computing on Enterprise Supply Chain Security Management

At the enterprise level of supply chain securitymanagement, the impact of network technologies such as theInternet of Things (IoT) and cloud computing has beensubstantial. These technologies have influenced various aspectsof supply chain management, including operational modes andsecurity management methods. Traditional supply chainmanagement relied on outdated communication methods andmanual interventions for decision-making and processing. Incontrast, modern information and communication technologiesoffer immediacy and strong correlation. This article leveragesIoT data interconnection technology to construct supply chainmanagement models, incorporating efficient cloud computingalgorithms. The exploration of supply chain securitymanagement models encompasses participant perspectives,activity modes, and module operations, with subsequent datacomparisons. Experimental results reveal an average accuracyof approximately 89.60% and a remarkable prediction accuracyof 98.48% for benefits. Stability testing demonstrates anaverage stability of around 96.02% after multiple iterations.The research highlights the significant application potential ofIoT and cloud computing technologies in enhancing supplychain security management, providing enterprises with moreeffective tools for this purpose

Sign Language Recognition using Machine Learning

Deaf and dumb people communicate with others and within their own groups by using sign language. Beginning with the acquisition of sign gestures, computer recognition of sign language continues until text or speech is produced. There are two types of sign gestures: static and dynamic. Both gesture recognition systems, though static gesture recognition is easier to use than dynamic gesture recognition, are crucial to the human race. In this survey, the steps for sign language recognition are detailed. Examined are the data collection, preprocessing, transformation, feature extraction, classification, and outcomes. There were also some recommendations for furthering this field of study