Domain Adaptation has emerged as an important development in Speech Recognition systems for improving the transcription accuracy of the input audio. This study explores the enhancement of Domain-specific Automatic Spe...
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Cucumbers are crucial agricultural commodities worldwide, necessitating production enhancements and quality maintenance. However, several diseases can easily hamper cucumber production if not classified and detected e...
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This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for hu...
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With the help of Expense Tracker, you can easily and intuitively manage your revenue and costs while keeping track of your everyday, weekly, monthly, and annual spending. Users may choose the kind of expenditure, add ...
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Traditional methods of plant disease detection are cumbersome and prone to errors that cannot be avoided. Plant disease detection can help prevent crop losses and ensure food security. The system employs state-of-the-...
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The paper proposes an eXplainable Artificial Intelligence model that can be utilized in credit risk the board and, specifically, in estimating the dangers that emerge when credit is acquired utilizing shared loaning s...
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The longest simple path and snake-in-a-box are combinatorial search problems of considerable research interest. Recent work has recast these problems as special cases of a generalized longest simple path (GLSP) framew...
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Effective resource distribution and speed efficiency are key to giving people the best service in cloud computer settings. Dynamic task ordering is a key part of reaching these goals because it uses available resource...
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In recent years, there has been a growing emphasis on information security, with major companies introducing cybersecurity teams to ensure the safety of data. However, there is a lack of convenient channels for genera...
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ISBN:
(纸本)9798400716874
In recent years, there has been a growing emphasis on information security, with major companies introducing cybersecurity teams to ensure the safety of data. However, there is a lack of convenient channels for general users to engage in cybersecurity protection, leaving them with no choice but to rely on various antivirus software installations to safeguard their privacy and financial assets. This phenomenon is particularly evident in the digital transformation of healthcare and medical information systems, where the substantial amount of digitized patient data has become a crucial asset within hospital systems. Unfortunately, it has also made healthcare systems potential targets for cyberattacks. The databases of major hospitals have become vulnerable to malicious virus invasions, posing significant threats to patient privacy and the operational integrity of healthcare institutions. Faced with such threats, there is an increasing need for comprehensive cybersecurity protection mechanisms. To address this issue, we have developed the Universal Binary Malware Analysis Framework (UBMAF), an easily accessible binary file analysis framework for the general public. UBMAF integrates multiple open-source static and dynamic analysis tools into an automated module, deployed as Software as a Service (SaaS) in the cloud for healthcare and medical systems. This eliminates the need for users to install applications, and the framework interface is optimized for intuitive usability. During the usage process, users can freely choose module combinations. After uploading files to UBMAF, the framework conducts corresponding tool analyses or file processing based on the selected modules. Ultimately, it provides users with downloadable results and analysis reports. This design enables large healthcare and medical systems to quickly and conveniently enhance their cybersecurity defenses while ensuring the security of digital medical data, effectively addressing the challenges brought abo
Sarcasm is a form of irony that requires readers or listeners to interpret its intended meaning by considering context and social cues. Machine learning classification models have long had difficulty detecting sarcasm...
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