gen_ai_paper.ipynb

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          "text": [
            "    Year     Country  AI Publications  GenAI Publications\n",
            "0   2015  Bangladesh                0                   0\n",
            "1   2016  Bangladesh                2                   0\n",
            "2   2017  Bangladesh                0                   0\n",
            "3   2018  Bangladesh                0                   0\n",
            "4   2019  Bangladesh                0                   0\n",
            "..   ...         ...              ...                 ...\n",
            "95  2020       China               41                   0\n",
            "96  2021       China               98                   0\n",
            "97  2022       China              403                   0\n",
            "98  2023       China              475                  54\n",
            "99  2024       China             1356                  45\n",
            "\n",
            "[100 rows x 4 columns]\n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "\n",
        "def find_papers_by_country_and_year(country, year, keywords):\n",
        "    base_url = \"https://api.crossref.org/works\"\n",
        "    query = \" \".join(keywords)\n",
        "\n",
        "    params = {\n",
        "        \"filter\": f\"from-pub-date:{year}-01-01,until-pub-date:{year}-12-31\",\n",
        "        \"query\": query,\n",
        "        \"rows\": 1000\n",
        "    }\n",
        "\n",
        "    response = requests.get(base_url, params=params)\n",
        "    response.raise_for_status()\n",
        "\n",
        "    data = response.json()\n",
        "    items = data.get(\"message\", {}).get(\"items\", [])\n",
        "\n",
        "    paper_count = 0\n",
        "\n",
        "    for item in items:\n",
        "        author_list = item.get(\"author\", [])\n",
        "        for author in author_list:\n",
        "            affiliation_list = author.get(\"affiliation\", [])\n",
        "            for affiliation in affiliation_list:\n",
        "                if country.lower() in affiliation.get(\"name\", \"\").lower():\n",
        "                    paper_count += 1\n",
        "                    break\n",
        "\n",
        "    return paper_count\n",
        "\n",
        "# List of countries\n",
        "countries = [\"Bangladesh\", \"India\", \"Pakistan\", \"Sri Lanka\", \"Nepal\", \"Bhutan\", \"Maldives\", \"Afghanistan\", \"USA\", \"China\"]\n",
        "\n",
        "# Define years\n",
        "years = list(range(2015, 2024 + 1))\n",
        "\n",
        "# Keywords\n",
        "general_ai_ml_nlp = [\n",
        "    \"Artificial Intelligence\", \"AI\", \"Machine Learning\", \"ML\",\n",
        "    \"Neural Networks\", \"Deep Learning\", \"Deep Neural Networks\",\n",
        "    \"Natural Language Processing\", \"NLP\", \"Text Mining\",\n",
        "    \"Computational Linguistics\", \"Speech Recognition\", \"Voice Recognition\",\n",
        "    \"Image Recognition\", \"Computer Vision\", \"Pattern Recognition\",\n",
        "    \"Data Mining\", \"Predictive Analytics\", \"Supervised Learning\",\n",
        "    \"Unsupervised Learning\", \"Reinforcement Learning\", \"Self-supervised Learning\",\n",
        "    \"Zero-shot Learning\", \"Few-shot Learning\", \"Transfer Learning\",\n",
        "    \"Bayesian Networks\", \"Probabilistic Models\", \"Graph Neural Networks\",\n",
        "    \"Cognitive Computing\", \"Knowledge Representation\", \"Semantic Analysis\",\n",
        "    \"Sentiment Analysis\", \"Recommendation Systems\", \"Clustering Algorithms\",\n",
        "    \"Classification Algorithms\", \"Support Vector Machines\", \"Decision Trees\",\n",
        "    \"Random Forests\", \"Gradient Boosting\", \"Ensemble Methods\"\n",
        "]\n",
        "generative_ai = [\n",
        "    \"Generative AI\", \"Large Language Models\", \"LLM\", \"ChatGPT\",\n",
        "    \"GPT-3\", \"GPT-4\", \"GPT-2\", \"OpenAI GPT\", \"Transformer models\",\n",
        "    \"Attention Mechanism\", \"Self-attention\", \"DALL-E\", \"BERT\", \"RoBERTa\",\n",
        "    \"DistilBERT\", \"ALBERT\", \"T5\", \"XLNet\", \"ELECTRA\",\n",
        "    \"Generative Pretrained Transformer\", \"GPT\", \"Autoregressive Models\",\n",
        "    \"Masked Language Models\", \"Variational Autoencoders\", \"VAEs\",\n",
        "    \"Generative Adversarial Networks\", \"GANs\", \"Conditional GANs\",\n",
        "    \"CycleGAN\", \"StyleGAN\", \"DeepFake\", \"Text Generation\",\n",
        "    \"Language Model\", \"Sequence-to-Sequence Models\", \"Seq2Seq\",\n",
        "    \"Prompt Engineering\", \"Prompt Tuning\", \"Text-to-Image Generation\",\n",
        "    \"Image Synthesis\", \"Neural Machine Translation\", \"NMT\",\n",
        "    \"Dialogue Systems\", \"Conversational AI\", \"Natural Language Generation\",\n",
        "    \"NLG\", \"Text Summarization\", \"Story Generation\"\n",
        "]\n",
        "\n",
        "# Initialize dataframe\n",
        "data = []\n",
        "\n",
        "# Loop over each country and year\n",
        "for country in countries:\n",
        "    for year in years:\n",
        "        ai_publications = find_papers_by_country_and_year(country, year, general_ai_ml_nlp)\n",
        "        genai_publications = find_papers_by_country_and_year(country, year, generative_ai)\n",
        "        data.append([year, country, ai_publications, genai_publications])\n",
        "\n",
        "# Create dataframe\n",
        "df = pd.DataFrame(data, columns=[\"Year\", \"Country\", \"AI Publications\", \"GenAI Publications\"])\n",
        "\n",
        "# Display dataframe\n",
        "print(df)"
      ]
    },
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      "source": [
        "df.head(20)"
      ],
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              "    Year     Country  AI Publications  GenAI Publications\n",
              "0   2015  Bangladesh                0                   0\n",
              "1   2016  Bangladesh                2                   0\n",
              "2   2017  Bangladesh                0                   0\n",
              "3   2018  Bangladesh                0                   0\n",
              "4   2019  Bangladesh                0                   0\n",
              "5   2020  Bangladesh                0                   0\n",
              "6   2021  Bangladesh                4                   0\n",
              "7   2022  Bangladesh                8                   1\n",
              "8   2023  Bangladesh                8                   1\n",
              "9   2024  Bangladesh                7                   6\n",
              "10  2015       India                2                   0\n",
              "11  2016       India                2                   0\n",
              "12  2017       India                3                   0\n",
              "13  2018       India                4                   0\n",
              "14  2019       India               12                   0\n",
              "15  2020       India               78                   0\n",
              "16  2021       India               77                   0\n",
              "17  2022       India              161                   2\n",
              "18  2023       India              185                  36\n",
              "19  2024       India              143                  10"
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2015,\n        \"max\": 2024,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2023,\n          2016,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"USA\",\n          \"India\",\n          \"Bhutan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AI Publications\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151,\n        \"min\": 0,\n        \"max\": 1356,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          98,\n          9,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GenAI Publications\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 54,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          10,\n          0,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('gen_ai_publication_data.csv', index = None)"
      ],
      "metadata": {
        "id": "vH4rHdTG84kn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}