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Applications & Awareness in Technology

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Home/Science & Technology/Applications & Awareness in Technology/Page 47

Mains Answer Writing Latest Questions

Sankalp Tikku
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Sankalp TikkuBegginer
Asked: July 25, 2024In:
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bio-techologyquestionscience
  1. kalpana P
    kalpana P Begginer

    Drug Discovery: AI-powered algorithms can analyze vast amounts of data to identify potential drug candidates, predict their efficacy, and optimize their design. Genetic Engineering: Machine learning can help design and optimize genetic editing tools like CRISPR-Cas9, enabling precise and efficient gRead more

    • Drug Discovery: AI-powered algorithms can analyze vast amounts of data to identify potential drug candidates, predict their efficacy, and optimize their design.
    • Genetic Engineering: Machine learning can help design and optimize genetic editing tools like CRISPR-Cas9, enabling precise and efficient genome editing.
    • Personalized Medicine: AI can analyze genomic data to predict patient responses to treatments, enabling personalized treatment plans and targeted therapies.

    These technologies are transforming biotechnology by:

    • Accelerating Research: AI and machine learning can analyze vast amounts of data, identify patterns, and make predictions, accelerating the research process.
    • Improving Accuracy: AI-powered algorithms can reduce errors and improve the accuracy of biotechnological processes.
    • Enabling Personalization: AI can help tailor treatments to individual patients, enabling personalized medicine and improving patient outcomes.

    Companies like Deep Genomics are using AI to discover new RNA medicines, while others are leveraging machine learning to develop novel genetic therapies. The integration of AI and machine learning is revolutionizing biotechnology, enabling faster, more accurate, and more personalized solutions for human health.

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[Deleted User]
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[Deleted User]
Asked: July 25, 2024In:
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  1. Mitesh Singh
    Mitesh Singh Begginer

    To make sure that any student doesn’t matter who they are, regardless of the economic or ethnic background, has access to quality computer science education we can take several steps: Inclusive Curriculum and Teaching: Push for cultivation of culturally responsive and inclusive curricula. Promote teRead more

    To make sure that any student doesn’t matter who they are, regardless of the economic or ethnic background, has access to quality computer science education we can take several steps:

    Inclusive Curriculum and Teaching: Push for cultivation of culturally responsive and inclusive curricula. Promote teacher training in the use of different instructional strategies. Enhance diverse role models in computer science.

    Accessibility and Resources: Struggle to provide technology and internet access. Encourage free educational resources. Advocate for scholarships targeting underrepresented groups.

    Support Systems: Start mentorship programs as well as peer support groups. Get families and communities more involved in supporting students.

    Extracurricular Opportunities: Foster coding clubs, compete with one another, hold workshops, organize camps etc..

    Policy Advocacy: Champion policies on compulsory computer science education and equality-based initiatives

    Remote Learning: Take online courses and embrace virtual collaborative tools wherever applicable. These strategies help create an inclusive environment where all students can succeed in computer science.

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[Deleted User]
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[Deleted User]
Asked: July 25, 2024In:
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  1. Kiran Shiyal
    Kiran Shiyal Begginer
    This answer was edited.
    How can we assess students' progress in computer science beyond traditional exams?

    'Assessing students' progress in computer science beyond traditional exams can be achieved through:-  Project-based evaluations: Have students work on real-world projects that demonstrate their coding skills, problem-solving abilities, and creativity. Peer review and feedback: Encourage students toRead more

    ‘Assessing students’ progress in computer science beyond traditional exams can be achieved through:-

    •  Project-based evaluations: Have students work on real-world projects that demonstrate their coding skills, problem-solving abilities, and creativity.
    • Peer review and feedback: Encourage students to review and provide constructive feedback on each other’s projects, promoting critical thinking and collaboration.
    •  Code repositories and version control: Use platforms like GitHub to track students’ coding progress, collaboration, and version control skills.
    •  Competitions and hackathons: Participate in coding challenges, hackathons, or coding Olympiads to assess problem-solving skills under time pressure.
    • Self-assessment and reflection: Encourage students to reflect on their learning journey, set goals, and identify areas for improvement.
    • Class discussions and presentations: Engage students in technical discussions, presentations, and debates to assess their understanding and communication skills.
    •  Authentic assessments: Use real-world scenarios, case studies, or industry-specific problems to assess students’ ability to apply computer science concepts to practical situations.

    These alternative assessment methods provide a comprehensive picture of students’ skills, knowledge, and progress in computer science.

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[Deleted User]
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[Deleted User]
Asked: July 25, 2024In:
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  1. Bhavana Notla
    Bhavana Notla Begginer

    To ensure that computer science education remains relevant and aligned with rapidly evolving technology, several strategies are essential. Firstly, curriculum development must be flexible and responsive, incorporating emerging technologies, trends, and methodologies such as artificial intelligence,Read more

    To ensure that computer science education remains relevant and aligned with rapidly evolving technology, several strategies are essential. Firstly, curriculum development must be flexible and responsive, incorporating emerging technologies, trends, and methodologies such as artificial intelligence, machine learning, and cybersecurity. This requires collaboration between educators, industry experts, and researchers to identify current and future needs.

    Secondly, continuous professional development for teachers is crucial to keep them updated with the latest advancements in technology and teaching methods. This includes workshops, seminars, and partnerships with industry to bridge the gap between academia and practical application.

    Thirdly, fostering interdisciplinary approaches within computer science education can prepare students to adapt to evolving technology. Integrating subjects like data science, engineering, and ethics into the curriculum enhances students’ ability to innovate and address real-world challenges.

    Lastly, establishing partnerships with technology companies and research institutions can provide access to state-of-the-art facilities, internships, and mentorship opportunities, enriching students’ learning experiences.

    By embracing these strategies, computer science education can effectively keep pace with technological advancements, ensuring that students are equipped with the skills and knowledge needed to thrive in an ever-changing digital landscape.

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Riddhi Patel
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Riddhi PatelBegginer
Asked: July 25, 2024In: ,
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What are the key differences between TCP and UDP?

  1. Bhavana Notla
    Bhavana Notla Begginer

    TCP (Transmission Control Protocol) and UDP (User Datagram Protocol) are two fundamental protocols used in computer networking, each with distinct characteristics suited for different types of applications. TCP is connection-oriented, ensuring reliable communication by establishing a connection betwRead more

    TCP (Transmission Control Protocol) and UDP (User Datagram Protocol) are two fundamental protocols used in computer networking, each with distinct characteristics suited for different types of applications.

    TCP is connection-oriented, ensuring reliable communication by establishing a connection between sender and receiver before data exchange. It guarantees delivery of data packets in sequence, performs error-checking, and retransmits lost packets, making it ideal for applications where data integrity and order are critical, such as web browsing, email, and file transfer.

    UDP, in contrast, is connectionless and does not guarantee reliable delivery or order of packets. It offers lower overhead and faster transmission since it lacks error-checking and retransmission mechanisms. UDP is preferred for applications where speed and efficiency are prioritized over reliability, such as streaming media, online gaming, and real-time communication.

    Key differences include TCP’s reliability through error detection, flow control, and retransmission, whereas UDP offers simplicity, low latency, and is suitable for time-sensitive applications. TCP ensures data arrives intact and in order but may have higher latency, whereas UDP sacrifices reliability for speed and efficiency. Choosing between TCP and UDP depends on the specific requirements of the application in terms of reliability, speed, and overhead.

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[Deleted User]
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[Deleted User]
Asked: July 25, 2024In:
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  1. Bhavana Notla
    Bhavana Notla Begginer

    Addressing the gender gap in computer science and encouraging more girls to pursue IT careers requires a concerted effort across various levels and sectors. Firstly, promoting early exposure to coding and technology through inclusive educational initiatives in schools can spark interest and confidenRead more

    Addressing the gender gap in computer science and encouraging more girls to pursue IT careers requires a concerted effort across various levels and sectors.

    Firstly, promoting early exposure to coding and technology through inclusive educational initiatives in schools can spark interest and confidence among girls. Providing role models and mentorship programs where girls can interact with successful women in IT fields can also inspire and guide them towards pursuing careers in technology.

    Secondly, challenging stereotypes and biases through awareness campaigns and inclusive curriculum development can foster a supportive and equitable learning environment. Schools and educational institutions should actively promote diversity and inclusion in their programs and faculty.

    Thirdly, creating opportunities for hands-on experiences, internships, and industry collaborations can help bridge the gap between classroom learning and real-world application, demonstrating the relevance and excitement of IT careers.

    Moreover, establishing supportive networks and communities, both online and offline, where girls can share experiences, collaborate on projects, and receive encouragement can strengthen their interest and commitment to pursuing IT careers.

    By implementing these strategies collaboratively across education, industry, and community sectors, we can work towards closing the gender gap in computer science and ensuring that all individuals, regardless of gender, have equal opportunities to thrive and succeed in IT careers.

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Abinaya S
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Abinaya SBegginer
Asked: July 25, 2024In:
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  1. sanjeev sonwani
    sanjeev sonwani Begginer

    AbstractBackgroundMachine learning (ML) and artificial intelligence (AI) tools have revolutionized genetic disorder prediction from genomic data. In this respect, ML algorithms can discover relationships among widely spread genomic sequences which might be in relation to indicators of certain genetiRead more

    AbstractBackgroundMachine learning (ML) and artificial intelligence (AI) tools have revolutionized genetic disorder prediction from genomic data. In this respect, ML algorithms can discover relationships among widely spread genomic sequences which might be in relation to indicators of certain genetic conditions. LSTMs have the capability to handle bigger and more complicated datasets than what traditional statistical methods can support.

    A prime use case is supervised learning – algorithms trained on labeled datasets consisting of the genetic data from individuals with and without known disorders (Image 3). These models are taught to anticipate the occurrence of genetic disorders in new, untagged genomic sequences based on learning features and patterns associated with these conditions.

    This is why deep learning, a sub-area of ML work so well in this area. Deep learning models such convolutional and recurrent neural networks have the capability or retaining complex patterns in genomic data. For example, CNNs can understand the spatial arrangement of nucleotides while clarity with needles data being a sequence tends to make it more suitable for RNN.

    These regions make up for interesting gene-sequences.

    AI enables to find new genetic variants for diagnostics without any supervised learning, which would reveal unmet gene-disorder associations. Furthermore, AI-based models are able to pool different types of data (e.g., clinicodemographic and environmental factors) equally well- leading to higher prediction precision.

    On the other hand, ML and AI combined advances our comprehension of genetic disorders exponentially leading to tailor-made treatments as well as early interventions.

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Yogi Patel
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Yogi PatelBegginer
Asked: July 25, 2024In:
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How does federated learning ensure model accuracy when data is distributed across multiple, possibly heterogeneous, devices?

accuracydatadata distributionfederated learning
  1. Bhavana Notla
    Bhavana Notla Begginer
    This answer was edited.

    Federated learning ensures model accuracy in distributed environments by leveraging the collective intelligence of devices while respecting data privacy and local constraints. Here’s how it works: Instead of centralizing data on a single server, federated learning enables training models directly onRead more

    Federated learning ensures model accuracy in distributed environments by leveraging the collective intelligence of devices while respecting data privacy and local constraints. Here’s how it works: Instead of centralizing data on a single server, federated learning enables training models directly on user devices (e.g., smartphones, IoT devices), where data is generated. Each device computes model updates based on local data while keeping the raw data decentralized and private.

    To ensure accuracy:

    1.Collaborative Learning: Model updates from multiple devices are aggregated periodically or iteratively, typically by a central server or collaboratively among devices. This aggregation balances out variations in local data distributions and improves overall model accuracy.

    2.Differential Privacy: Techniques like differential privacy are employed to add noise or anonymize data during model aggregation, preserving individual privacy while maintaining utility and accuracy of the aggregated model.

    3.Adaptive Learning: Algorithms are designed to adapt to heterogeneous data distributions and varying computational capabilities of devices. This adaptability ensures that the federated model remains effective across diverse devices and environments.

    4.Iterative Refinement: Models are iteratively refined through multiple rounds of federated learning, where insights from initial rounds inform subsequent training, gradually improving accuracy without compromising data privacy.

    By distributing computation and learning directly at the edge (on devices), federated learning optimizes model accuracy while respecting data privacy, making it well-suited for applications in healthcare, IoT, and other sensitive domains where data locality and privacy are paramount concerns.

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Yogi Patel
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Yogi PatelBegginer
Asked: July 25, 2024In:
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How does federated learning enhance data privacy compared to traditional centralized machine learning approaches?

aidata privacyfederated learningmachine learning
  1. Mitesh Singh
    Mitesh Singh Begginer

    Federated Learning (FL) and Traditional Machine Learning (TML) differ significantly in data handling and privacy: Traditional Machine Learning: Centralized Data: TML requires collecting and storing all data in a central server. Privacy Concerns: Centralizing data can expose it to security risks andRead more

    Federated Learning (FL) and Traditional Machine Learning (TML) differ significantly in data handling and privacy:

    Traditional Machine Learning:
    Centralized Data: TML requires collecting and storing all data in a central server.
    Privacy Concerns: Centralizing data can expose it to security risks and privacy breaches.

    Federated Learning:
    Decentralized Data: FL allows models to be trained across multiple devices without transferring raw data to a central server.
    Enhanced Privacy: By keeping data on local devices and only sharing model updates, FL reduces the risk of data breaches and enhances user privacy.

    Privacy Enhancement:
    Data Minimization: FL minimizes the amount of data shared, limiting exposure.
    Local Processing: Sensitive data stays on user devices, reducing the chance of unauthorized access.

    As a Advisor, it’s important to recognize that FL offers a more privacy-conscious approach to machine learning by maintaining data on local devices and avoiding centralized data collection.

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