A Comparative Review of Expert Systems, Recommender Systems, and Explainable AI

Ravi, Malini; Negi, Atul; Chitnis, Sanjay

doi:10.1109/i2ct54291.2022.9824265

Cited by 18 publications

(6 citation statements)

References 41 publications

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“…Explicit methods examine user interactions, while other methods involve artificial immune systems, interest scores, and opinion mining. Implementing recommender systems, however, faces challenges related to handling large datasets [14].…”

Section: Essential Concepts About Atrsmentioning

confidence: 99%

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A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining: A Novel Approach

Mudavath,

Negi

2024

Preprint

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The use of automated systems is helpful for the social transformation to an advanced society. This research paper presents a baggage item recommendation system that leverages FastText word embeddings and Association Rule Mining (ARM) to deliver personalized suggestions to users. The methodology involves several key steps. First, the baggage data is collected and preprocessed, including loading pre-trained Fast-Text word embeddings for text representation. The Phase-I recommendation system is then developed, using FastText embeddings to calculate similarity scores between user input and existing items. Next, the Phase-II recommendation system is extended to incorporate user search history by recording searches and storing them in a data frame. In Phase-III, ARM is applied to the user search history, generating associationrules that enrich the recommendation process. The final stage, Phase-IV, combines FastText and ARM-based recommendations, providing users with more accurate and relevant suggestions. The research evaluates the recommendation model’s performance using metrics such as coverage, support, confidence, lift, leverage, and conviction. The proposed approach achieves enhanced item recommendations, offering insights intouser behavior and preferences, and opens opportunities for personalized recommendations, marketing strategies, and product optimization.

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Section: Essential Concepts About Atrsmentioning

confidence: 99%

“…Knowledge-based filtering relies on user-provided information to generate recommendations, while demographic filtering tailors suggestions based on user profiles. Context-aware filtering takes into account contextual factors in the recommendation process [14], [8].…”

Section: Essential Concepts About Atrsmentioning

confidence: 99%

A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining: A Novel Approach

Mudavath,

Negi

2024

Preprint

Self Cite

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“…Pada sistem ini metode inferensi yang digunakan yaitu Forward Chaining. Forward Chaining adalah teknik pencarian yang dimulai dengan fakta yang diketahui, kemudian mencocokkan faktafakta tersebut dengan bagian IF dari rules IF-THEN [18]. Bila ada fakta yang cocok dengan IF, maka rule tersebut dieksekusi.…”

Section: Pendahuluanunclassified

Sistem Pakar Menggunakan Forward Chaining dalam Mendeteksi Tingkat Keparahan Skizofrenia

Surya

2022

jidt

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Penelitian Riset Kesehatan Dasar (Riskesdas) pada tahun 2018 menunjukkan prevalensi Skizofrenia di Indonesia sebanyak 7% per 1.000 rumah tangga. Cakupan indikator Penderita Gangguan Jiwa Mendapatkan Pengobatan dan Tidak Ditelantarkan (PGJMPTD) nasional sebesar 38,14%, dan Provinsi Sumatera Barat untuk indikator yang sama dengan angka 45,58% berada pada urutan provinsi keempat teratas dengan nilai 45,58%. Penelitian ini bertujuan membangun sistem terhadap indikator Skizofrenia secara tepat. Sistem yang dibangun dalam bentuk Sistem Pakar. Sistem Pakar merupakan kemampuan dari komputer mengubah pengetahuan-pengetahuan dari manusia ke dalam komputer dan dapat membantu menanggulangi permasalahan yang hanya bisa diselesaikan oleh pakar. Sistem pakar deteksi tingkat keparahan Skizofrenia merupakan sistem yang mengadopsi pengetahuan Psikiater dalam menentukan tingkat keparahan Skizofrenia seorang pasien kejiwaan. Sistem pakar ini dibuat dengan metode Forward Chaining. Tujuan dari Sistem Pakar ini adalah untuk membuktikan bahwa metode Forward Chaining dapat diimplementasikan dalam pembuatan Sistem Pakar ini. Selain itu, Sistem Pakar dapat memberikan manfaat untuk membantu seorang Psikiater dalam melakukan tes penentuan tingkat keparahan Skizofrenia pasien. Data yang digunakan dalam penelitian ini sebanyak 20 data Rekam Medis pasien, berupa gejala-gejala penyakit serta data diagnosa pasien oleh dokter Spesialis Kedokteran Jiwa. Selanjutnya, data diolah menggunakan metode Inferensi Forward Chaining dan di disajikan dalam bentuk aplikasi dengan bahasa pemograman PHP. Hasil dari penelitian ini berupa 18 data valid dan 2 data tidak valid sehingga didapatkan nilai akurasi sebesar 90%. Sistem Pakar dengan metode Forward Chaining cocok daan dapat digunakan untuk mendeteksi tingkat keparahan penyakit Skizoprenia.

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“…Several diverse fields have embraced the explainable component of AI, prioritising trustworthiness over accuracy. XAI has been applied in drug discovery [99,100], industrial applications [101,102], gaming [103,104], neurological disorders [105,106,107], neuroscience [108,109] and recommender systems [110,111]. This tremendous growth has led to several XAI-based review articles in the healthcare domain in the past years.…”

Section: Introductionmentioning

confidence: 99%

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Vimbi¹,

Shaffi²,

Mahmud

et al. 2023

Preprint

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Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable results on various metrics primarily due to recent advancements in computational capabilities. However, AI models used for Computer Aided Diagnosis (CAD) have limited acceptance and trust due to their typical {Blackbox} nature. Consequently, explainable AI (XAI) is needed to justify the trustworthiness of the machine's predictions. Approach: In this work, we undertake a systematic review based on PRISMA guidelines on research articles investigating Alzheimer's Disease (AD) prediction with XAI for the past decade. The review was driven by carefully formulated research questions (RQ) which helped categorize AI models into different XAI methods(Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, etc) and frameworks (LIME, SHAP, GradCAM, LRP, etc.). Summary: This categorisation provides broad coverage of the interpretation spectrum from intrinsic (model-specific, ante-hoc models) to complex patterns (model-agnostic, post-hoc models) and by taking local explanations to a global scope. Additionally, the study categorizes merit in terms of different forms of interpretation to provide in-depth insight into the factors that support the clinical diagnosis of AD. Finally, limitations and open research challenges are outlined, and possible prospects are presented.

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A Comparative Review of Expert Systems, Recommender Systems, and Explainable AI

Cited by 18 publications

References 41 publications

A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining: A Novel Approach

A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining: A Novel Approach

Sistem Pakar Menggunakan Forward Chaining dalam Mendeteksi Tingkat Keparahan Skizofrenia

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

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