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How to write poems which people might find appealing to read?
Crafting poems that people enjoy means a combination of creativity, emotion and technical ability. Here are some big factors to be aware of: 1) Find Your Voice: Build your own unique voice reflecting who you are as a person. Being authentic allows readers to remember what you had shared. 1 EmotionalRead more
Crafting poems that people enjoy means a combination of creativity, emotion and technical ability. Here are some big factors to be aware of:
1) Find Your Voice: Build your own unique voice reflecting who you are as a person. Being authentic allows readers to remember what you had shared.
1 Emotional Connection: Relate to common emotions and experiences. Intense emotions are what allow your poem and the reader to connect, so love them; or rather abuse them..
3 Imagery and Descriptions: Paint Pictures in the Reader’s Mind A good romance book is full of vivid, evocative imagery. The sensory details draw the reader into the poem, and require engagement on their part.
4 Sound and Rhythm Listen to your words as you shape them; be aware of the sound. That means how using rhythm and meter or devices such as alliteration, assunance, rhyme can make your poem more musical.
5 .Conciseness: Be concise and precise with your language. Every word should serve a purpose, contributing to the overall impact of the poem.
6 Structure and Form: Experiment with different structures and forms, from free verse to sonnets, to find what best suits your style and the poem’s theme.
7 Read Widely: Read a wide range of poetry to understand different styles and techniques. This can inspire you and help you learn what resonates with readers.
8 Edit and Revise: Writing a poem is often a process of revision. Refine your work to enhance clarity, coherence, and emotional depth.
By combining these elements, you can create poems that captivate and resonate with readers, inviting them to see the world through your words.
See lessHow can machine learning and artificial intelligence be used to predict genetic disorders from genomic data?
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.
See lessBiotechnology Contribution to global food security
Biotechnology is essential to food security globally, as it helps increase yields of crops; increases nutrition content and the value of crop harvests; introduces new varieties that withstand various environmental challenges. A key contribution of the organization is to produce genetically modifiedRead more
Biotechnology is essential to food security globally, as it helps increase yields of crops; increases nutrition content and the value of crop harvests; introduces new varieties that withstand various environmental challenges. A key contribution of the organization is to produce genetically modified (GM) crops, which are resistant to pests, diseases and herbicides. This contributes to less crop losses and the really important benefit of reducing costly chemical treatments, meaning more sustainable farming and a higher output per acre.
Biotechnology helps in creating crops which are resistant to biotic and abiotic stresses such as drought, salinity and temperature fluctuations. With climate change increasingly impacting agricultural productivity around the world, these are essential attributes. A more water-efficient and drought-resistant crop is possible in dry areas, which will ensure food production during bad times
Biofortification, a major contribution towards improving crop nutritional value. Rice, wheat and maize – staples for more than four billion people – have already been genetically modified to produce higher levels of important nutrients including vitamins.
See lessHow AI is harmful for humans?
AI can harm human in different ways, Major Issue- Loss of jobs Automation The more advanced AI systems become, the less of something called general labor will be required and people with no livelihood (a form of human intelligence that can perform a variety of tasks) will lose their jobs causing ecoRead more
AI can harm human in different ways, Major Issue- Loss of jobs Automation The more advanced AI systems become, the less of something called general labor will be required and people with no livelihood (a form of human intelligence that can perform a variety of tasks) will lose their jobs causing economic chaos. Selection bias and discrimination. When these are biases that should be avoided (such as hiring managers using biased data to inform a HR AI system) the situation is troubling; it may cause discrimination against specific groups, or even amplify discrimnation in society where whole groups of people become systematically marginalized.
Another major issue is the question of privacy, as AI needs a lot data in order to operate successfully. This equates to data harvesting and potential invasions of privacy. There are also worries around abuse of AI – such as in the instances like that possible with facial recognition, or deepfakes (which can too be used to create fake identities for bad purposes).
Moreover, the creation of autonomous weapons
See less