scholarly journals An Artificial Intelligence that Discovers Unpredictable Chemical Reactions

2020 ◽  
Author(s):  
Dario Caramelli ◽  
Jaroslaw Granda ◽  
Dario Cambié ◽  
Hessam Mehr ◽  
Alon Henson ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Chemical Reactions ◽  
Multicomponent Reaction ◽  
Cascade Reaction ◽  
One Pot ◽  
Single Substrate ◽  
Set Up

<p><b>We present an artificial intelligence, built to autonomously explore chemical reactions in the laboratory using deep learning. The reactions are performed automatically, analysed online, and the data is processed using a convolutional neural network (CNN) trained on a small reaction dataset to assess the reactivity of reaction mixtures. The network can be used to predict the reactivity of an unknown dataset, meaning that the system is able to abstract the reactivity assignment regardless the identity of the starting materials. The system was set up with 15 inputs that were combined in 1018 reactions, the analysis of which lead to the discovery of a ‘multi-step, single-substrate’ cascade reaction and a new mode of reactivity for methylene isocyanides. <i>p</i>-Toluenesulfonylmethyl isocyanide (TosMIC) in presence of an activator reacts consuming six equivalents of itself to yield a trimeric product in high (unoptimized) yield (47%) with formation of five new C-C bonds involving <i>sp</i>-<i>sp<sup>2</sup></i> and <i>sp</i>-<i>sp<sup>3</sup></i> carbon centres. A cheminformatics analysis reveals that this transformation is both highly unpredictable and able to generate an increase in complexity like a one-pot multicomponent reaction.</b></p>

scholarly journals An Artificial Intelligence that Discovers Unpredictable Chemical Reactions

2020 ◽  
Author(s):  
Dario Caramelli ◽  
Jaroslaw Granda ◽  
Dario Cambié ◽  
Hessam Mehr ◽  
Alon Henson ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Chemical Reactions ◽  
Multicomponent Reaction ◽  
Cascade Reaction ◽  
One Pot ◽  
Single Substrate ◽  
Set Up

<p><b>We present an artificial intelligence, built to autonomously explore chemical reactions in the laboratory using deep learning. The reactions are performed automatically, analysed online, and the data is processed using a convolutional neural network (CNN) trained on a small reaction dataset to assess the reactivity of reaction mixtures. The network can be used to predict the reactivity of an unknown dataset, meaning that the system is able to abstract the reactivity assignment regardless the identity of the starting materials. The system was set up with 15 inputs that were combined in 1018 reactions, the analysis of which lead to the discovery of a ‘multi-step, single-substrate’ cascade reaction and a new mode of reactivity for methylene isocyanides. <i>p</i>-Toluenesulfonylmethyl isocyanide (TosMIC) in presence of an activator reacts consuming six equivalents of itself to yield a trimeric product in high (unoptimized) yield (47%) with formation of five new C-C bonds involving <i>sp</i>-<i>sp<sup>2</sup></i> and <i>sp</i>-<i>sp<sup>3</sup></i> carbon centres. A cheminformatics analysis reveals that this transformation is both highly unpredictable and able to generate an increase in complexity like a one-pot multicomponent reaction.</b></p>

scholarly journals Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

2022 ◽  
Vol 30 (7) ◽  
pp. 1-23
Author(s):  
Hongwei Hou ◽  
Kunzhi Tang ◽  
Xiaoqian Liu ◽  
Yue Zhou
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Research Result ◽  
Customer Information ◽  
Rural Governance ◽  
Artificial Intelligence Technology ◽  
Reasonable Prediction

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

scholarly journals Aspect Based Sentimen Analysis Opini Publik Pada Instagram dengan Convolutional Neural Network

2019 ◽  
Vol 1 (2) ◽  
pp. 50-57
Author(s):  
Muhammad Arief Rahman ◽  
Herman Budianto ◽  
Esther Irawati Setiawan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network

Internet sebagai sarana informasi dan komunikasi sudah sangat dikenal di kalangan masyarakat dalam menawarkan kemudahan dan fleksibilitas yang cukup memadai ketika menjadi media. Oleh karena itu opini publik terhadap Operator Telekomunikasi merupakan hal yang sangat penting untuk dijadikan patokan. Namun, untuk mengevaluasi umpan balik online itu, bukan masalah sederhana. Kadang-kadang ketika menganalisis ulasan online yang berkembang pesat ini, menjadi sulit untuk mengkategorikan apakah opini pelanggan puas atau tidak puas terhadap produk dan layanan. Selain itu, sebagai bagian dari peningkatan kualitas mereka, organisasi seperti jasa ini perlu mengklasifikasikan aspek produk dan layanan yang paling disukai pelanggan. Deep Learning adalah area baru dalam penelitian Machine Learning, yang telah diperkenalkan dengan tujuan menggerakkan Machine Learning lebih dekat dengan salah satu tujuan aslinya yaitu Artificial Intelligence. Deep Learning adalah tentang belajar beberapa tingkat representasi dan abstraksi yang membantu untuk memahami data seperti gambar, suara, dan teks. Convolutional Neural Network adalah salah satu contoh metode Deep Learning. Metode Convolutional Neural Network diharapkan dapat digunakan dalam pengimplementasian opini publik untuk keperluan data training yang dikumpulkan dari beragam data yang dianotasikan kelas sentimennya secara otomatis. Hasil dari penelitian menunjukkan dari 4 aspek dan 3 sentimen maka didapatkan nilai rata-rata precision, recall, dan f1-score adalah precision  97.6%, recall 84%, f1-score 90.3%. Bisa disimpulkan score representation ini dapat digunakan untuk klasifikasi sentimen.

scholarly journals PENGKLASIFIKASIAN GENRE MUSIK INDONESIA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Carl Ray Wairata ◽  
Ericks Rachmat Swedia ◽  
Margi Cahyanti
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network

Pada zaman sekarang sudah terdapat banyak sekali teknologi Artificial Intelligence. Artificial Intelligence sendiri memiliki beberapa sub bab, salah satunya adalah Machine Learning dan Deep Learning merupakan salah sub bab dari Machine Learning itu sendiri. Convolutional Neural network (CNN) adalah salah satu jenis neural network yang biasa digunakan pada data gambar. Pada penelitian ini akan digunakan untuk mengklasifikasikan genre musik dengan cara mengonversi data pada lagu menjadi sebuah gambar yang kita sebut spektogram. Pada penelitian akan mengimplementasikan CNN dalam mengategorikan 3 genre musik di Indonesia yakni; dangdut, Jazz dan Pop. Pada penelitian ini terdapat 100 lagu untuk masing-masing genre sebagai data setnya. Tujuan dari penelitian ini sendiri adalah mengoptimalkan tingkat akurasi dalam pengategorian genre musik menggunakan model CNN. Berdasarkan penelitian yang sudah dilakukan didapatkan bahwa penggunaan 35 epoch memiliki tingkat akurasi yang optimal yakni; tingkat akurasi tes sebesar 81,33% dan tingkat akurasi validasi sebesar 100%. Implementasi ini diharapkan dapat menentukan kategori dalam musik.

scholarly journals Literature Review Artificial Intelligence Deteksi Hasil Ctscan Paru-Paru Pasien Terjangkit COVID-19

2021 ◽  
Vol 2 (3) ◽  
pp. 502-516
Author(s):  
Sadly Syamsuddin ◽  
Kalfin Alloto'dang ◽  
Risnayanti Andi Djamro ◽  
Ahyuna
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Literature Review ◽  
Convolutional Neural Network ◽  
Ct Scan

Penyakit Virus Corona 19 (COVID-19) merupakan penyakit menular yang disebabkan oleh virus corona yang baru-baru ini ditemukan. Saat ini COVID-19 menjadi fenomena permasalahan untuk kita semua namun hingga sekarang belum ada obat yang ditemukan ampuh dalam mengatasinya. Persoalan lain adalah pada proses pendeteksian orang terjangkit. Hasil pendeteksian Covid-19 menggunakan PCR Swap masih dianggap sangat lambat dan menggunakan Rapid Tes bahkan dianggap kurang meyakinkan dengan melihat beberapa kasus yang ada sebelumnya. Tujuan penelitian ini untuk pendeteksian orang terjangkit COVID-19 lebih cepat dengan tingkat akurasi yang tinggi menggunakan metode Artificial Intelligence yang lebih khusus menggunakan Deep Learning arsitektur Convolutional Neural Network (CNN). Metode penelitian yang digunakan adalah literature review, dimana artikel dikumpulkan dan diproses menggunakan aplikasi mendeley, kriteria artikel yang digunakan adalah yang diterbitkan tahun 2020 yang berkaitan dengan penanganan COVID-19 khususnya yang memanfaatkan Artificial Intelligence dalam pembahasannya. Dengan mengumpulkan dan membahas beberapa penelitian yang ada maka dapat dikatakan bahwa dengan menggunakan Artificial Intelligence sistem dapat mendeteksi terjangkitnya seseorang melalui analisa pola yang ada pada hasil CT Scan Paru dengan memanfaatkan tingkat akurasi data latih yang ada.

scholarly journals Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

2020 ◽  
Author(s):  
Alejandro Lopez-Rincon ◽  
Alberto Tonda ◽  
Lucero Mendoza-Maldonado ◽  
Daphne G.J.C. Mulders ◽  
Richard Molenkamp ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Diagnostic Methods ◽  
Neural Network Classifier ◽  
Explainable Artificial Intelligence ◽  
Primer Sets ◽  
Virus Strains

ABSTRACTIn this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from available repositories, separating the genome of different virus strains from the Coronavirus family with considerable accuracy. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are first validated on samples from other repositories, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets on existing datasets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from NGDC, separating the genome of different virus strains from the Coronavirus family with accuracy 98.73%. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from NCBI and GISAID, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

Deep Learning in Computational Neuroscience

2020 ◽  
pp. 43-63
Author(s):  
Sanjay Saxena ◽  
Sudip Paul ◽  
Adhesh Garg ◽  
Angana Saikia ◽  
Amitava Datta
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Computational Neuroscience ◽  
Deep Model ◽  
The World ◽  
Current Scenario

Computational neuroscience is inspired by the mechanism of the human brain. Neural networks have reformed machine learning and artificial intelligence. Deep learning is a type of machine learning that teaches computers to do what comes naturally to individuals: acquire by example. It is inspired by biological brains and became the essential class of models in the field of machine learning. Deep learning involves several layers of computation. In the current scenario, researchers and scientists around the world are focusing on the implementation of different deep models and architectures. This chapter consists the information about major architectures of deep network. That will give the information about convolutional neural network, recurrent neural network, multilayer perceptron, and many more. Further, it discusses CNN (convolutional neural network) and its different pretrained models due to its major requirements in visual imaginary. This chapter also deliberates about the similarity of deep model and architectures with the human brain.

scholarly journals A Reactivity First Approach to Autonomous Discovery of New Chemistry

2021 ◽  
Author(s):  
Dario Caramelli ◽  
Jaroslaw Granda ◽  
Hessam Mehr ◽  
Dario Cambié ◽  
Alon Henson ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Chemical Reactions ◽  
Neural Network Model ◽  
Photochemical Reaction ◽  
Cascade Reaction ◽  
High Yield ◽  
Discovery System ◽  
Single Substrate ◽  
Autonomous Discovery

<p></p><p></p><p>We present a robotic chemical discovery system capable of learning the generalized notion of reactivity using a neural network model that can abstract the reactivity from the identity of the reagents. The system is controlled using an algorithm that works in conjunction with this learned knowledge, the robot was able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown datasets, regardless the identity of the starting materials. The system identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, but also some unpredictable reactions that yielded new molecules. The search was done within a budget of 15 inputs combined in 1018 reactions, which allowed us not only to discover a new photochemical reaction, but also a new reactivity mode for a well-known reagent (<i>p</i>-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of six equivalents of TosMIC in a ‘multi-step, single-substrate’ cascade reaction yielding a trimeric product in high yield (47% unoptimized) with formation of five new C-C bonds involving <i>sp</i>-<i>sp<sup>2</sup></i> and <i>sp</i>-<i>sp<sup>3</sup></i> carbon centres. Analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.</p><br><p></p><p></p>

scholarly journals Automatic Detection of COVID-19 Infection from Chest X-ray using Deep Learning

2020 ◽  
Author(s):  
kishore Medhi ◽  
Md. Jamil ◽  
Iftekhar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Respiratory System ◽  
Data Repository ◽  
X Ray ◽  
Network Method ◽  
Chest X Ray

COVID-19 infection has created a panic across the globe in recent times. Early detection of COVID-19 infection can save many lives in the pre-vailing situation. This virus affects the respiratory system of a person and creates white patchy shadows in the lungs. Deep learning is one of the most effective Artificial Intelligence techniques to analyse chest X-ray images for efficient and reliable COVID-19 screening. In this paper, we have proposed a Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images. To validate the performance of the proposed system, chest X-ray images of more than 150 confirmed COVID-19 patients from the Kaggle data repository are used in the experimentation. The results show that the proposed system identifies the cases with an accuracy of 93%.

Harnessing the Power of Artificial Intelligence for Modelling and Understanding Cultural Heritage Data

2020 ◽  
pp. 357-376 ◽  
Author(s):  
Raissa Garozzo ◽  
Carmelo Pino ◽  
Cettina Santagati ◽  
Concetto Spampinato
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Cultural Heritage ◽  
Convolutional Neural Network ◽  
Learning Process ◽  
Semantic Retrieval ◽  
Recent Trends ◽  
Santa Maria ◽  
The Church

This chapter combines traditional artificial intelligence (AI) concepts, i.e., computational ontologies, with more recent trends, i.e., deep learning for content-based semantic retrieval in Cultural Heritage. More specifically, the proposed AI-empowered system employs computational ontologies for modelling photographs of religious historical buildings. The ontology, besides supporting data-modelling and concept-level annotation, guides a learning process – implemented through Convolutional Neural Network (CNN) – for automated image categorization and retrieval. The whole system has been tested on the ruins of the church of Santa Maria delle Grazie in Misterbianco, Catania, Italy, showing satisfactory performance.

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