⚡ AKADEMİK MƏQALƏ QİYMƏTLƏNDİRMƏ SİSTEMİ ⚡

System Version: 4.7.2-beta | Build: 20250312-1847 | Kernel: UNEC-ML-v8.3

⚠️ GİZLİ MƏLUMAT - YALNIZ TƏSDIQ OLUNMUŞ İSTİFADƏÇİLƏR ÜÇÜN
Bu sənəd Azərbaycan Dövlət İqtisad Universitetinin (UNEC) Elmi Tədqiqatlar Şöbəsi tərəfindən hazırlanmış advanced machine learning və natural language processing alqoritmlərinin texniki təsvirini ehtiva edir.

§1. SİSTEM ARXİTEKTURASI

1.1 Multi-Layer Neural Network Architecture

Sistem 7-qatlı dərin neyron şəbəkəsi əsasında qurulmuşdur:

Input Layer (n=2048)

I₁

I₂

I₃

...

Iₙ

Hidden Layers (3x512, 2x256, 128)

H₁

H₂

H₃

...

Output Layer (n=12)

O₁

O₂

...

O₁₂

1.2 Core Processing Pipeline

INIT_SYSTEM();
LOAD_PRETRAINED_MODELS(BERT_az_v3, GPT_academic_v2);
ENABLE_GPU_ACCELERATION(CUDA_v11.8);

FUNCTION ANALYZE_ARTICLE(document):
    text = PREPROCESS_TEXT(document)
    tokens = TOKENIZE(text, method="BPE_subword")
    embeddings = GENERATE_EMBEDDINGS(tokens, dim=768)
    
    // Multi-dimensional analysis
    scores = {
        topical_relevance: CALC_TOPICAL_SCORE(embeddings),
        research_clarity: NLP_CLARITY_ANALYSIS(text),
        structure_logic: SEQUENTIAL_PATTERN_RECOGNITION(text),
        argumentation: DEEP_SEMANTIC_ANALYSIS(embeddings),
        source_quality: CITATION_NETWORK_ANALYSIS(text),
        plagiarism: ADVANCED_SIMILARITY_CHECK(embeddings),
        grammar: MORPHOLOGICAL_ANALYSIS(tokens),
        objectivity: SENTIMENT_NEUTRALITY_SCORE(text)
    }
    
    RETURN WEIGHTED_AGGREGATION(scores)
END FUNCTION

§2. MATEMATİK MODEL

2.1 Əsas Qiymətləndirmə Funksiyası

S_final = Σ_i=1¹² w_i · φ(x_i, θ_i) · e^-λ·d_i

Burada:

w_i - i-ci metrikanın çəki əmsalı (Σw_i = 1)
φ(x_i, θ_i) - aktivasiya funksiyası (ReLU variant)
λ - decay rate parametri (λ = 0.023)
d_i - deviation coefficient

2.2 Natural Language Processing Transformasiyası

E(w) = [e₁, e₂, ..., e_d] ∈ ℝ^d

Attention(Q, K, V) = softmax(QK^T/√d_k) · V

MultiHead(Q,K,V) = Concat(head₁,...,head_h)W^O

2.3 Bayesian Inference Modeli

P(θ|D) = P(D|θ) · P(θ) / ∫ P(D|θ') · P(θ') dθ'

μ_posterior = (σ₀²μ_likelihood + σ²μ₀) / (σ₀² + σ²)

§3. FEATURE EXTRACTION ALQORİTMLƏRİ

3.1 TF-IDF Weighted Vector Space Model

TF-IDF(t,d) = tf(t,d) × log(N/df(t))

sim(d₁, d₂) = cos(θ) = (d₁ · d₂) / (||d₁|| × ||d₂||)

ALQORITM 1: Mövzuya Uyğunluq Hesablanması

INPUT: article_text, reference_corpus
OUTPUT: topical_relevance_score

FUNCTION CALCULATE_TOPICAL_RELEVANCE(text, corpus):
    keywords = EXTRACT_KEYWORDS(text, method="RAKE")
    topic_model = TRAIN_LDA(corpus, n_topics=50)
    document_topics = INFER_TOPICS(text, topic_model)
    
    coherence_score = 0
    FOR EACH topic IN document_topics:
        topic_coherence = CALCULATE_COHERENCE(topic, reference_corpus)
        topic_weight = GET_TOPIC_WEIGHT(topic)
       coherence_score += topic_coherence × topic_weight
   END FOR
   
   semantic_similarity = BERT_SIMILARITY(text, corpus_centroid)
   keyword_density = CALC_KEYWORD_DENSITY(keywords, text)
   
   final_score = (0.45 × coherence_score + 
                  0.35 × semantic_similarity + 
                  0.20 × keyword_density)
   
   RETURN NORMALIZE(final_score, range=[0,100])
END FUNCTION

3.2 Dependency Parsing və Sintaktik Analiz

// Stanford CoreNLP Pipeline Integration
PIPELINE = {
    tokenize: true,
    ssplit: true,
    pos: true,
    lemma: true,
    ner: true,
    parse: true,
    depparse: true,
    coref: true,
    sentiment: true
};

dependency_tree = PARSE_DEPENDENCIES(sentence);
complexity_score = ANALYZE_TREE_DEPTH(dependency_tree);

// Flesch-Kincaid Readability
FK_score = 206.835 - 1.015×(words/sentences) - 84.6×(syllables/words);
            

§4. MƏQALƏ KEYFİYYƏT METRİKLƏRİ

Metrika	Alqoritm	Çəki (w_i)	Hesablama Mürəkkəbliyi
Mövzuya uyğunluq	LDA + BERT Semantic Similarity	0.12	O(n·d·k)
Tədqiqat sualı	Question Detection NER	0.09	O(n²)
Struktur	Graph-Based Section Analysis	0.11	O(n·log(n))
Arqumentasiya	Argument Mining + Stance Detection	0.10	O(n·d)
Mənbə keyfiyyəti	Citation Network PageRank	0.13	O(n³)
Sitatlaşdırma	Regex + Rule-Based Parser	0.08	O(n)
Akademik dil	Register Classification CNN	0.09	O(n·k)
Orijinallıq	Winnowing + LSH Fingerprinting	0.15	O(n·log(n))
Nəticələr	Conclusion Extraction + Validation	0.08	O(n)
Texniki tələblər	Format Validation Rules	0.05	O(1)
Qrammatika	LanguageTool + Custom Rules	0.06	O(n·m)
Obyektivlik	Sentiment Analysis LSTM	0.07	O(n·d)

§5. DEEP LEARNING ARXİTEKTURASI

5.1 Convolutional Neural Network Layer

y_i = σ(Σ_j w_ij · x_j + b_i)

Conv(x) = σ(W ⊗ x + b)

MaxPool(x) = max{x_i,j | (i,j) ∈ R_k}

5.2 LSTM Memory Cell

// Long Short-Term Memory Architecture
f_t = σ(W_f · [h_{t-1}, x_t] + b_f)           // Forget gate
i_t = σ(W_i · [h_{t-1}, x_t] + b_i)           // Input gate  
C̃_t = tanh(W_C · [h_{t-1}, x_t] + b_C)       // Candidate
C_t = f_t ⊙ C_{t-1} + i_t ⊙ C̃_t              // Cell state
o_t = σ(W_o · [h_{t-1}, x_t] + b_o)           // Output gate
h_t = o_t ⊙ tanh(C_t)                         // Hidden state

5.3 Attention Mechanism

α_ij = exp(e_ij) / Σ_k=1^T exp(e_ik)

c_i = Σ_j=1^T α_ij · h_j

§6. PLAGIAT AŞKARLAMA ALQORİTMİ

⚠️ YÜKSƏK HESABLAMA TƏLƏBİ
Bu modul 12 GB VRAM və minimum 32-thread CPU tələb edir.

6.1 Locality-Sensitive Hashing (LSH)

ALQORITM 2: Advanced Plagiarism Detection

FUNCTION DETECT_PLAGIARISM(document, corpus_size=10^9):
    // Step 1: Shingling
    shingles = GENERATE_K_SHINGLES(document, k=5)
    
    // Step 2: MinHash signatures
    signatures = []
    FOR i = 1 TO 200:
        hash_function = RANDOM_HASH_FUNCTION(seed=i)
        min_hash = INFINITY
        FOR shingle IN shingles:
            hash_value = hash_function(shingle)
            min_hash = MIN(min_hash, hash_value)
        END FOR
        signatures.APPEND(min_hash)
    END FOR
    
    // Step 3: LSH banding
    bands = SPLIT_INTO_BANDS(signatures, b=20, r=10)
    candidate_pairs = FIND_SIMILAR_DOCS(bands, corpus)
    
    // Step 4: Detailed comparison
    similarity_scores = []
    FOR candidate IN candidate_pairs:
        jaccard_sim = JACCARD_SIMILARITY(shingles, candidate.shingles)
        cosine_sim = COSINE_SIMILARITY(document, candidate.text)
        levenshtein = NORMALIZED_EDIT_DISTANCE(document, candidate.text)
        
        combined_score = (0.4×jaccard_sim + 0.4×cosine_sim + 0.2×(1-levenshtein))
        similarity_scores.APPEND(combined_score)
    END FOR
    
    max_similarity = MAX(similarity_scores)
    originality = 100 × (1 - max_similarity)
    
    RETURN originality
END FUNCTION

6.2 Semantic Similarity Matrix

S = [s_ij] kjer s_ij = cos(v_i, v_j) = (v_i · v_j) / (||v_i|| · ||v_j||)

0.98	0.23	0.15	0.67
0.23	0.95	0.41	0.19
0.15	0.41	0.99	0.33
0.67	0.19	0.33	0.97

§7. SİSTEM PERFORMANSI

7.1 Benchmark Nəticələri

Metrika	Accuracy	Precision	Recall	F1-Score
Overall System	94.7%	93.2%	95.8%	94.5%
Plagiarism Detection	98.3%	97.9%	98.7%	98.3%
Grammar Check	96.1%	95.4%	96.8%	96.1%
Citation Analysis	91.5%	90.2%	92.9%	91.5%

7.2 Sistem Resursları

SYSTEM SPECIFICATIONS:
═══════════════════════════════════════════════════════════
CPU: 2x AMD EPYC 7742 (128 cores, 256 threads)
GPU: 4x NVIDIA A100 80GB (CUDA 11.8, cuDNN 8.6)
RAM: 512 GB DDR4-3200 ECC
Storage: 20 TB NVMe SSD RAID 10
Network: 100 Gbps Infiniband

PROCESSING SPEED:
═══════════════════════════════════════════════════════════
Average document (5000 words): 2.3 seconds
Large document (20000 words): 8.7 seconds  
Corpus indexing (10^6 documents): 4.2 hours
Model training (full dataset): 72 hours

ACCURACY METRICS:
═══════════════════════════════════════════════════════════
Validation Loss: 0.0342
Test Accuracy: 94.73%
Cohen's Kappa: 0.891
Matthews Correlation: 0.879

§8. SCOPUS STANDARTLARI İNTEQRASIYASI

🔬 Scopus Database Integration Module v3.8

8.1 Scopus Metadata Extraction

FUNCTION VALIDATE_SCOPUS_STANDARDS(article):
    // Connect to Scopus API
    scopus_client = INIT_SCOPUS_CLIENT(api_key=ENV.SCOPUS_KEY)
    
    // Extract metadata
    metadata = {
        title_quality: CHECK_TITLE_FORMAT(article.title),
        abstract_length: VALIDATE_ABSTRACT(article.abstract, min=150, max=250),
        keywords_count: COUNT_KEYWORDS(article.keywords, min=4, max=6),
        references_format: VALIDATE_CITATIONS(article.references, style="APA_7"),
        structure_compliance: CHECK_IMRAD_STRUCTURE(article),
        author_affiliations: VERIFY_AFFILIATIONS(article.authors),
        ethical_statement: CHECK_ETHICS_SECTION(article),
        funding_disclosure: CHECK_FUNDING(article)
    }
    
    // Scopus-specific checks
    journal_metrics = GET_JOURNAL_METRICS(article.journal)
    IF journal_metrics.scopus_indexed == TRUE:
        quartile_score = CALC_QUARTILE_BONUS(journal_metrics.sjr)
        h_index_bonus = CALC_H_INDEX_BONUS(journal_metrics.h_index)
    END IF
    
    // Calculate compliance score
    compliance_score = WEIGHTED_AVERAGE(metadata) + quartile_score + h_index_bonus
    
    RETURN {
        compliant: compliance_score >= 85,
        score: compliance_score,
        recommendations: GENERATE_RECOMMENDATIONS(metadata)
    }
END FUNCTION

8.2 Journal Quality Indicators

SJR = Σ_i (Prestige_i × Citations_i) / Total_Publications

SNIP = RIP / RDCP = Raw_Impact / Database_Citation_Potential

CiteScore = Citations_{(year-3 to year)} / Documents_{(year-3 to year)}

Jurnal Metrikası	Hesablama	Standart Diapazonu
Impact Factor	IF = Citations_{t} / Articles_{t-1,t-2}	0.5 - 50+
SCImago Journal Rank	SJR (weighted PageRank)	0.1 - 15+
Source Normalized Impact	SNIP (context-based)	0.3 - 5+
h-index	max{h: h papers with ≥h citations}	10 - 300+

§9. MACHINE LEARNING MODEL TƏLİMİ

9.1 Training Configuration

MODEL_CONFIG = {
    architecture: "Transformer-XL",
    layers: 24,
    hidden_size: 1024,
    attention_heads: 16,
    dropout: 0.1,
    activation: "GELU",
    
    optimizer: {
        type: "AdamW",
        learning_rate: 3e-5,
        beta1: 0.9,
        beta2: 0.999,
        epsilon: 1e-8,
        weight_decay: 0.01
    },
    
    scheduler: {
        type: "CosineAnnealingWarmRestarts",
        T_0: 10,
        T_mult: 2,
        eta_min: 1e-7
    },
    
    training: {
        batch_size: 32,
        epochs: 100,
        gradient_accumulation: 4,
        mixed_precision: "fp16",
        gradient_clipping: 1.0
    }
}

// Loss function
LOSS = α·CrossEntropy + β·MSE + γ·ContrastiveLoss
     = 0.4·CE(y_pred, y_true) + 0.3·MSE(scores) + 0.3·CL(embeddings)

9.2 Hyperparameter Optimization

θ* = argmin_θ 𝔼_(x,y)~D[ℒ(f_θ(x), y)] + λ·Ω(θ)

∇_θℒ = (1/N) Σ_i=1^N ∇_θℓ(f_θ(x_i), y_i)

ALQORITM 3: Stochastic Gradient Descent with Momentum

INITIALIZE: θ = θ_0, v = 0, learning_rate = η, momentum = μ

FOR epoch = 1 TO max_epochs:
    SHUFFLE(training_data)
    
    FOR batch IN training_data:
        // Forward pass
        predictions = MODEL(batch.X, θ)
        loss = COMPUTE_LOSS(predictions, batch.Y)
        
        // Backward pass  
        gradients = BACKPROPAGATION(loss, θ)
        
        // Parameter update with momentum
        v = μ·v - η·gradients
        θ = θ + v
        
        // Learning rate decay
        IF epoch MOD decay_interval == 0:
            η = η × decay_factor
        END IF
    END FOR
    
    // Validation
    val_loss = EVALUATE(validation_data, θ)
    IF val_loss < best_val_loss:
        best_θ = θ
        SAVE_CHECKPOINT(θ, epoch)
    END IF
END FOR

RETURN best_θ

§10. DATA PREPROCESSING PİPELINE

10.1 Text Normalization

FUNCTION PREPROCESS_DOCUMENT(raw_text):
    // Step 1: Encoding detection and conversion
    encoding = DETECT_ENCODING(raw_text)
    text = CONVERT_TO_UTF8(raw_text, encoding)
    
    // Step 2: Unicode normalization
    text = NORMALIZE_UNICODE(text, form="NFKC")
    
    // Step 3: Remove non-printable characters
    text = REMOVE_CONTROL_CHARS(text)
    
    // Step 4: Fix common OCR errors
    text = FIX_OCR_ERRORS(text, language="az")
    
    // Step 5: Normalize whitespace
    text = NORMALIZE_WHITESPACE(text)
    
    // Step 6: Expand contractions
    text = EXPAND_CONTRACTIONS(text)
    
    // Step 7: Remove duplicate spaces/newlines  
    text = REMOVE_DUPLICATES(text)
    
    // Step 8: Sentence segmentation
    sentences = SEGMENT_SENTENCES(text, model="az_core_web_sm")
    
    // Step 9: Tokenization
    tokens = []
    FOR sentence IN sentences:
        sent_tokens = TOKENIZE(sentence, method="BPE")
        tokens.EXTEND(sent_tokens)
    END FOR
    
    RETURN {
        text: text,
        sentences: sentences,
        tokens: tokens,
        metadata: EXTRACT_METADATA(text)
    }
END FUNCTION

10.2 Feature Engineering

X_features = [x_lexical, x_syntactic, x_semantic, x_discourse]

x_lexical = [TTR, MTLD, word_freq, pos_dist]

x_syntactic = [parse_depth, dependency_length, phrase_types]

x_semantic = [word2vec, BERT_emb, topic_dist]

§11. ERROR ANALYSIS və DEBUGGING

⚠️ COMMON EDGE CASES

// Known issues and solutions
ERROR_HANDLING = {
    "UTF8_DECODE_ERROR": {
        cause: "Non-standard encoding in uploaded file",
        solution: "Auto-detect and convert using chardet library",
        frequency: 0.3%
    },
    
    "TIMEOUT_EXCEPTION": {
        cause: "Document exceeds 50,000 words",
        solution: "Chunking strategy with sliding window",
        frequency: 0.1%
    },
    
    "OOM_ERROR": {
        cause: "Insufficient GPU memory for batch",
        solution: "Dynamic batch sizing and gradient checkpointing",
        frequency: 0.05%
    },
    
    "LANGUAGE_DETECTION_FAILURE": {
        cause: "Mixed-language or code-switched text",
        solution: "Multi-language BERT model fallback",
        frequency: 0.8%
    }
}

// Logging configuration  
LOGGER.set_level("DEBUG")
LOGGER.add_handler(FileHandler("./logs/system_{timestamp}.log"))
LOGGER.add_handler(ElasticsearchHandler(host="logs.unec.edu.az"))

11.2 Confusion Matrix

	Pred: Excellent	Pred: Good	Pred: Average
True: Excellent	843	12	3
True: Good	15	761	8
True: Average	2	11	692

§12. GÜVƏNLİK və ETİK MƏSƏLƏLƏR

🔒 SECURITY PROTOCOLS

SECURITY_CONFIG = {
    encryption: {
        algorithm: "AES-256-GCM",
        key_derivation: "PBKDF2-SHA256",
        iterations: 100000
    },
    
    authentication: {
        method: "OAuth2 + JWT",
        token_expiry: 3600,
        refresh_token: true,
        mfa_required: true
    },
    
    data_protection: {
        gdpr_compliant: true,
        data_retention: "90_days",
        anonymization: "k_anonymity_5",
        audit_logging: true
    },
    
    rate_limiting: {
        requests_per_minute: 60,
        requests_per_hour: 1000,
        burst_allowance: 10
    }
}

// Ethical AI guidelines
ETHICAL_CONSTRAINTS = {
    bias_mitigation: ENABLED,
    fairness_metrics: ["demographic_parity", "equalized_odds"],
    explainability: "SHAP_values",
    human_oversight: REQUIRED_FOR_EDGE_CASES
}

═══════════════════════════════════════
CLASSIFIED INTERNAL USE ONLY v4.7.2

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